The control of the postural system is one of the fundamental neurophysiological mechanisms of the human body. It is fundamental to ensuring balance against gravity and fixing body orientation, and functions as a reference frame for perception–action coupling while efficiently dealing with the external world. Postural control is a dynamic process that requires sensory detection of body motions and integration of sensorimotor information within the central nervous system. In more detail, the central nervous system triggers the execution of appropriate musculoskeletal responses in order to obtain an equilibrium between destabilizing and stabilizing forces [1].

It has been shown, in the reference literature of physiatric medicine, that measurements of postural stability are critical for determining predictors of performance [2], for evaluating musculoskeletal injuries [3], for determining the effectiveness of physical training and rehabilitation treatments [4], and to provide injury prevention through the study

**Citation:** Cesari, P.; Cristani, M.; Demrozi, F.; Pascucci, F.; Picotti, P.M.; Pravadelli, G.; Tomazzoli, C.; Turetta, C.; Workneh, T.C.; Zenti, L. Towards Posture and Gait Evaluation through Wearable-Based Biofeedback Technologies. *Electronics* **2023**, *12*, 644. https://doi.org/10.3390/ electronics12030644

Academic Editors: Jikui Luo and Riccardo Pernice

Received: 13 December 2022 Revised: 24 January 2023 Accepted: 26 January 2023 Published: 28 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

of injury risk-factor analysis [3,5]. The body's motion is mainly based on the integration of the proprioceptive, visual, and vestibular inputs [6]. Afferent proprioceptive inputs are conveyed to different levels of the central nervous system [7–9]; however, most of them remain unconscious. The joint positions and movement sensing (kinaesthesia) are the expressions of the conscious component, but postural control is primarily based on the unconscious component [9]. Specifically for the antigravity movements, proprioceptive control represents the expression of the effectiveness of the stabilizing reflexes in controlling vertical stability [8]. In fact, antigravity movements are the activities that counteract gravity and postural instability with at least a phase of single-limb stance [6]. In this way, proprioceptive input represents the most relevant sensory system in the maintenance of static postural stability at all ages and fitness levels [10]. This topic is also relevant in neurodegenerative diseases such as Parkinson's disease (PD). For example, PD patients with postural instability have worse reactions to brief perturbations, more stance sway, and trouble switching between tasks. Moreover, quantifying balance changes in early and moderate-stage PD and the comparison to healthy subjects using clinical assessments of balance and musculoskeletal activation is paramount, primarily if performed through less invasive and costly systems [11,12].

Many tools to detect musculoskeletal activation have been used in sport and rehabilitative medicine. Mainly, electromyography (EMG) is employed for this purpose. However, EMGs are not yet widely used in combination with accelerometers for forecasting and customizing measures for the analysis of the human body's motion to achieve different goals. This is mainly due to the limited number of investigations that have been focusing on the nature of musculoskeletal response to a broad spectrum of stimuli able to identify the thresholds that establish the standard/ideal status of the postural system. Hence, there is a lack of low-cost technology that habilitates these measurements.

Furthermore, in the last decade, with the advent of the Internet of Things (IoT), embedded sensors have been integrated into personal devices such as smartphones and smartwatches. In several applications, sensors are integrated into clothes or other equipment/objects of daily life, becoming a central research topic due to their importance in many areas, including healthcare, interactive gaming, sports, and monitoring systems for general purposes in controlled and uncontrolled settings [13–15].

The primary purpose of the investigation we carried out in this project was to present a preliminary study on the design of a portable and reliable postural system prototype composed of HW and SW, adapted to diverse individual profiles concerning the performance viewpoint, from patients needing rehabilitation to top-level athletes. It is widely accepted in the community of psychiatric medicine that proper quantification of the postural system efficiency represents an essential assessment for improving the quality of life. However, most of the actual measurements are developed in a laboratory environment where natural movements are usually constrained by the instruments applied to subjects' bodies and the environment. This process is performed to distinguish, as precisely as possible, in the limits of the experimental setting, the roles of proprioception, visual and vestibular input using a low-cost and portable instrument. This habilitates the individuals to move freely and perform activities at home or in other uncontrolled environments (e.g., gyms or sports facilities) [5,16].

The technology integrated into such project includes state-of-the-art apparatus (i.e., force platform, EMG, and motion capture cameras) and wireless three-dimensional inertial units performing computation and data analysis over low-cost devices (i.e., sensors themselves, smartphones, and tablets) or cloud platforms.

In the project, we considered variants of these configurations' implementations to evaluate possible solutions to different settings. Subsequently, another essential goal will be the usability of the envisioned technology. Thus, the possibility to perform a comparative study with existing state-of-the-art validated systems is needed to obtain a well-ground assessment. Moreover, fundamental analysis of the specific characteristics of the exercises existing in the literature and a combinational calculation will define the subsequent exercise prescriptions; a multitude of exercise combinations will be available to satisfy the actual needs of the different clinical conditions. To identify these exercises, in future works, we are going to investigate the potential of reinforcement learning as a tool for customization (in order to adapt the response of the portable tool to the needs of the setting, such as medical diagnosis, performance test, training evaluation, and training progress measurements) and personalizing (in order to adapt to the individual variability of the applications mentioned above).

The project has a threefold scope:

Finally, this paper presents the experimental apparatuses and describes the experimental design strictly related to the goals mentioned above. The experiments conducted were structured for a final purpose: obtaining an experimental system for identifying the correct patterns to be devised in the project's future developments.

The rest of the paper is organized as follows. Section 2 provides an overview of the relevant references to similar studies. Section 3 discusses the project's expected research outcome, and Section 4 introduces the overall experimental design. Section 5 presents a preliminary evaluation of the data collected by the designed setup. Finally, Section 6 presents some conclusions and sketches further work.

#### **2. Related Work**

Posture is studied in two aspects: (a) postural control and (b) postural orientation. The former involves studying the positional control of the body in space and orientation. Instead, the latter involves studying the relationships between body segments. A neutral state (also known as neutral posture) is observed when the upper trunk and head are at zero degrees concerning the vertebral column. Subjects deviating from this neutral posture are said to have low stability that can provoke accelerated intervertebral disc (IVD) degeneration, damage, and misalignment of vertebrae, producing nerve compression that can cause radicular manifestations, such as sensorimotor deficiencies and pain in the involved regions [17]. When considering the maintenance of the vertical posture in everyday life situations, postural control might become a complex task that requires the ability to anticipate and compensate postural strategies when fast actions are performed and when environmental perturbations are applied [18]. How individuals control their preparatory and compensatory postural adjustments is still under debate [19]. Several mechanisms help individuals to keep their posture when task conditions change due to self-inflicted perturbation (e.g., I am suddenly moving my upper arm forward [20] or when somebody is pushing me [21]). These mechanisms are represented by changes in the activation levels of postural muscles called early postural adjustments (EPAs), starting up to 1000ms prior to the impact [22], and anticipatory postural adjustments (APAs), starting 0–150ms prior to the impact [23]. The primary role played by the EPAs is to adjust the posture and facilitate action planning. Typical examples are seen in preparation for making a step [24] or to avoid contact with an approaching object [25]. On the other hand, the function of APAs is to generate forces that act against an effect (mechanical) of a predictable perturbation [26]. Here we concentrate on these ecological motor tasks where individuals are challenged to control posture when facing a highly dynamic situation. The tasks selected in this experimental design involve sequences of actions that require the maintenance of stable posture while standing on an unstable proprioceptive platform and receiving in an unexpected or expected way a perturbation requiring sudden balance recovery. These motor tasks will help unveil the individual strategies adopted given the individual's level of

skill. Based on the literature background, a single limb stance is regularly used to examine the postural system [16,27–29],

EMG, electrocardiography (ECG), and inertial sensors integrated into wearables are emerging as promising low-cost and easily usable solutions in everyday life [13] and health care contexts [30]. Inertial measurement unit (IMU)-based movement identification can be achieved by statistical classification or be threshold-based [31]. Such statistical methods utilize supervised machine learning, which links features of a movement to possible movement states in terms of the observation's possibility [32]. Many of these studies are devoted explicitly to disabled people with diminished gait/posture abilities. This holds for multiple sclerosis patients [33] and Parkinson's disease sufferers [30,34,35]. The ability to monitor the gait of multiple sclerosis patients and provide correct biofeedback can help prevent falls and detect freezing (an aspect that can be fruitful also for Parkinson's patients) [33,36]. Prototype systems often include integrated sensors located on the ankles to track gait movements. Body sensors are positioned near the cervical vertebra or on the shoulders to monitor body posture [13]. Many systems can also measure parameters that might be difficult to provide manually, such as the maximum acceleration of the patients during standing up, or the time it takes from sitting to standing [37,38].

Moreover, the current diffusion of machine learning methods employed in gait, posture analysis, and feedback is not comprehensive, but a few significant results have already been achieved. A relevant group of investigations has been designed for decoding algorithms for brain–machine interactions (BMIs) that use the spiking activity as their control signal [39]. These approaches are powerful in devising usable technologies. Specifically, feedback for reinforcement-learning-based brain–machine interfaces using confidence metrics has been addressed [40]. Some studies show how to derive the required evaluative feedback from a biological source, using both the feedback's quantity and quality, and incorporate it into reinforcement learning controller architecture to maximize performance. Analogously, the Berlin BCI has developed an accurate system that works from the first session in BCI-naive Subjects [41].

An overview of the various steps in the brain–computer interface (BCI) cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject, and the effect of feedback on brain activity, is the focus of [30,42]. On the other hand, the role of technology for accelerated motor learning in sports is investigated in [43]. Finally, parallel man–machine training in ECG-based cursor control development is the subject of [30,44]. Some references should be given to smart environment previous investigations as a foundation of the method developed here, emphasizing some development related to energy management [45] and concerning the design of energy-efficient transmission protocols for wireless body area networks [46,47]. However, the systems mentioned above present many limitations related to accessibility, economic cost, size, battery life, intrusiveness, and usability (i.e., controlled and uncontrolled home or working context) environments.

#### **3. Expected Research Outcomes of the Project**

This section illustrates the project goals, reporting the most suitable application scenarios of the technology we envision and an overview of the presented architecture design. For better comprehension, the goals are presented from a top-down perspective.

#### *3.1. Application Scenarios*

Three different potential application scenarios have been devised under the supervision and collaboration of psychiatric medicine personal and sports training experts. Scenario 1 was a controlled environment enriched with a set of sensors to the extent that it makes this environment smart. Scenario 2 was set without specifying whether the performed activities were to be carried out indoors or outdoors. It is legitimate to suppose that

the environmental setting shall be relatively poor regarding available interactions, including the potential unavailability of an Internet connection. Scenario 3 identified variations determined by post-traumatic rehabilitation, personalized performance control over the evolution of illnesses with harmful consequences on the patient's stability, and training of athletes with special needs.

#### Scenario 1: Diagnostic Evaluation

*In the context of a psychiatric medical practice, a patient who suffers from postural instability due to a traumatic event (e.g., car accident) or neurodegenerative disorder is visited by medical personnel. The diagnostic process is assisted with the envisioned technology. The patient is asked to execute a sequence of three exercises: single-stance stability test, forward movement of arms with weights, and step-on gait on a free-range. During the exercises, the patient wears a jacket equipped with a set of sensors and interacts with a visual focus tool that helps her to identify a fixed point at a given distance. The jacket interacts with an application that works on the cloud, measures the reaction time or other variables, including anticipation's effectiveness in the movements, and provides the operator the possibility of marking progress in performance quality based on a fixed threshold that the operator can define concerning age, sex, and the clinical condition of the patient. The whole process is recorded on video, and the instrumental measures are saved on the patient's profile.*

#### Scenario 2: Sport performance benchmarking

*An athlete training for a sporting event is monitored by her coach. He provides her with a performance benchmark in line with the event requirements and expectations. The athlete has a given training period for preparing for the event and a performance level she has to accomplish in order to be competitive. The performance benchmarks have been defined by the coach based on the athletic preparation path of the athlete. While following the coach's requests, the athlete executes some training exercises while wearing the jacket described in Scenario 1. Every athlete's exercise is compared against the benchmark performance and consequently identified in terms of a negative gradient concerning the benchmark itself.*

#### Scenario 3: Rehabilitation Follow-Up

*During the rehabilitation period, a patient wearing the jacket described in Scenario 1 attends a program consisting of a series of exercises. Each step in the series requires comparing the performance with the provided reference benchmark defined by the psychiatrist during the diagnostic process. The patient measures are the same as in Scenario 2 and represent how a patient uses the jacket in a medical context.*

#### *3.2. Envisioned Technology*

Our project's aim is to design an accessible, low-cost, small, dedicated low-cost technological solution (i.e., Gait and Posture Smart Jacket (GPSJack)) suitable for the previously introduced application scenarios. Figure 1 shows a graphic representation of the envisioned technology where the subject is wearing the GPSJack and is immersed in the Gait and Posture Smart Environment (GPSEnv). Numbers 1–5 mark the sensors attached to the jacket, and number 6 marks the tablet application used by the top-level user. Medical and potentially sports training staff can interact with the GPSJack/GPSEnv through a tablet to guarantee total portability and versatility of the envisioned technological solutions.

**Figure 1.** Visual description of the GPSJack technology and application scenario.

Therefore, the GPSJack system represents the low-cost inertial sensor-based system, which is the project's final goal. GPSEnv represents the adaption of the existing state-ofthe-art technologies to the presented scenarios for validation purposes. The final GSP-Jack/GPSEnv architecture will provide to medical/sports staff the capability to perform the following actions:

Based on the above-defined functions, several background software instruments are required. The technologies for managing and analyzing the data from the sensors, which we may name the GPSJack Framework, have been envisioned. Nevertheless, machine-learningbased algorithms will guide the personalization of the benchmark process by employing intelligent reinforcement learning methods. Finally, since the proposed technology has its main applications in healthcare, it will provide, in addition to the classical data protection techniques, a physical protection layer that, based on the radio signals' propagation patterns, will habilitate the possibility of utilizing the tablet *if and only if* the tablet is under a certain distance (e.g., 5 m) from the GPSJack nodes.

#### *3.3. Architecture Workflow*

The system's architecture is composed of several modules, each one with a single responsibility, the logic model of which is reported in Figure 2.

**Figure 2.** Logic model of system architecture.

Every module is related to at least another one:

**Evaluator:** responsible for the evaluation of data and automatic comparison with benchmarks;

**Sensor:** deals with wearable sensors, collecting and normalizing data;

**Machine Learning:** gathers data and induces models.

The User Interface's output is an ordered set of "environments", which are one of the inputs of the Configurator. The Configurator continuously computes, and at given times uses models from the Machine Learning module to produce a set of feasible "exercises". These exercises are to be given to the Trainer, whose outputs are chains of exercises, also named "paths", to be executed by patients (or athletes). At different times, different paths are possible due to the work of the Trainer. The data are then gathered from the Sensor module and sent to the Evaluator to be stored, visualized, and compared with benchmarks. They are also used to devise possible "paths" and exercises to be delivered as hints to the user. This is the responsibility of Machine Learning, which acts as a feedback generator for the whole system, enabling the system to enhance performances continuously.

#### **4. Methodology Design Workflow**

This section presents the overall project information concerning hardware composing the GPSEnv and GPSJack, and software regarding edge computation and the preliminary data analysis pipeline. To achieve the system's architectural requirements, we have designed a four-step method, illustrated in Figure 3.

**Figure 3.** GPSJack/GPSEnv system design workflow.

#### *4.1. Identification of Motor Tasks*

The motor tasks should contain specific characteristics. We aimed to select tasks that challenge postural stability and require the ability to foresee and anticipate the consequences of actions given the presence of a sudden perturbation that might change posture from a stable to an unstable state. In this way, we will be able to train fundamental motor skills such as action adaptation, compensation, and anticipation, while, on the other hand, measuring the performance of such skills. We will describe a typical trial involving a sequence of movements that satisfies the task requirements stated above:

**Figure 4.** The wooden board: bottom and lateral views.

We analyzed different experimental conditions to define the departure from the standard measurements by considering the same task while changing the biomechanical and perceptual conditions and testing different populations ranging from elite athletes to individuals affected by neuromuscular diseases. Figure 5 presents an overview of the force platform x-axis data of a motor task. As shown, it is composed by five different movements: (a) rotate, (b) in position, (c) APAs, (d) drop, and (e) recover. The task starts at most 10 s after the emission of an audio signal. Such a signal is later used during the manual synchronization of the GPSJack and GPSEnv data streams.

**Figure 5.** Overview of motor task phases: (**blue**) rotate, (**red**) in position, (**green**) APAs, (**purple**) drop, and (**yellow**) recover.

#### Data Collection Procedure

To validate the GPSJack system, subjects of different ages, genders, and motor backgrounds performed the introduced protocol of exercises. This protocol aimed to analyze the tested subjects' gait and posture using state-of-the-art instrumentation to identify the anticipatory movements and minimum requirements that the GPSJack nodes (as shown in Figure 1) will have to implement.

Figure 4 presents the proprioceptive board. Area dimensions were 0.45 m × 0.45 m, and height was 0.025 m. On the bottom of the surface, the board was touching the ground utilizing a beam glued along the board mid-line, having the same length as the wooden board, and being 0.025 m in height and 0.06 m in width. Figure 6, on the left, presents how the board was used during the exercise.

**Figure 6.** Experimental setup: the task.

#### *4.2. Data Collection Systems*

This section presents the instruments involved in the design of the GPSEnv and GPSJack systems. GPSEnv is defined as a state-of-the-art apparatus. GPSJack was designed to be a low-cost and long-battery-life system on chip (SoC).

#### 4.2.1. GPSEnv Apparatus

It performs gait and posture analyses based on the combination of three different instruments:

#### Force Platform

The forces in three orthogonal directions, along with the COP migration, are measured by a force platform (https://tinyurl.com/rhkktv4 accessed on 10 December 2022), coupled with a 6-channel strain gauge amplifier (https://tinyurl.com/zy2efpn8 accessed on 10 December 2022), with a sampling frequency of 1000 Hz, presenting a size of 0.9 m × 0.9 m. The AMTI Biomechanics Force Platform, model BP900900, features composite construction, resulting in a low-mass instrument with excellent frequency response. Specifically designed for the precise measurement of ground reaction forces, the BP900900 measures the three orthogonal force components, the moments about the three axes, and the center of pressure in the horizontal plane producing a total of eight outputs. The high sensitivity, low crosstalk, excellent repeatability, and long term stability of this platform make it ideal for research and clinical studies. The BP900900 is easy to use and is available with 1000, 2000, or 4000 pound (4450, 8900, or 17,800 Newtons) vertical capacity. This force platform is shown in Figure 6, on the left part, under the subject's feet, and in right image, represented virtually as platform 1.

#### Surface electromyography (EMG)

The surface EMG activity of sixteen postural muscles, on both sides of the body, is recorded using electrocardiographic electrodes located on the subject's body, as shown by the red markers in Figure 7. The muscles used are: the rectus abdominis (RA), erector spinae (ES), rector femoris (RF), biceps femoris (BF), vastus lateralis (VL), tensor fasciae latae (TL), tibialis anterior (TA), and soleus (SO). Guidelines from the http://www.seniam. org/ accessed on 10 December 2022 (Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles) are used to guarantee consistency in the muscles' anatomical localization. The https://fccid.io/VH6ZWTX07/User-Manual/User-Manual-903877 accessed on 10 December 2022 EMG system, produced by Aurion S.r.l., is used to collect and amplify EMG signals at a sampling rate of 1000 Hz.

**Figure 7.** Locations of retro-reflective markers (black) and EMG electrodes (red). EMGs were placed on both sides of the body.

#### Motion Capture Cameras

Concerning the kinematic analysis, five retro-reflective markers are attached to each subject. Markers' positions are located on the subject's body as shown by the black markers in Figure 7.

The markers are placed on the backs of both hands, on the forehead, on the lowerback, and on the lateral malleoulus on the dominant leg. The position, velocity, and acceleration of every marker are recorded at a sampling rate of 200 Hz, using eight motion capture cameras (https://www.evl.uic.edu/sjames/mocap/resources/Doc/MXhardware\_Reference.pdf accessed on 10 December 2022), featuring multiple high-speed processors that perform real-time proprietary image processing and Vicon Nexus 2.6 software.

#### 4.2.2. GPSJack Apparatus

The GPSJack prototype uses the nRF52840 system on chip (SoC), built over the 32-bit ARM CortexTM-M4 CPU with a floating-point unit running at 64 MHz. The nRF52840 is the most advanced member of the Nordic Semiconductor nRF52 Series SoC family (https: //www.nordicsemi.com/Products/Low-power-short-range-wireless/nRF52840 accessed on 10 December 2022). It is fully multi-protocol and capable of supporting Bluetooth 5, Bluetooth mesh, Thread, Zigbee, 802.15.4, ANT, and 2.4 GHz proprietary stacks [48]. Furthermore, the nRF52840 uses a sophisticated on-chip adaptive power management system achieving exceptionally low energy consumption.

This SoC interfaces with various electronic devices capable of perceiving different types of measurement of the movement context. In the setup we discuss here, the nRF52840 defines the core of each GPSJack node. On the same HW board, each SoC core communicates with various sensors, such as an accelerometer, gyroscope, and magnetometer. However, since the design of every single node of the GPSJack from scratch would require further targeted effort, and since the goal of the project is to show the suitability of the system in recognizing the different phases of the defined task, we made use of the Nordic Thingy 52 IoT Sensors kit shown in Figure 8. In future developments, the Nordic Thingy 52 can be replaced by dedicated data collection nodes based on the nRF52840 SoC, presenting reduced dimensions and integrating only sensors relevant to the scenario. It transmits data to/from its sensors and actuators to a receiver implemented through a PC, single board computing (SBC) (e.g., Raspberry pi 4 or Odroid H2+), or a mobile application running on a tablet or smartphone [48]. Extended device characteristics are listed in the following:

	- **–** Sampling frequency: up to 200 Hz;
	- **–** Full scale: up to 16 g for accelerometer and up to 2000 dps for gyroscope;

**Figure 8.** Nordic Thingy 52 board (**on the left**) and its usage in the data collection setup (**on the right**).

Nodes positions and number are not definitive, since their positions directly depend on the preliminary study carried out with the previously introduced instrumentation. The GP-SJack sampling frequency, the number of nodes, and the positions will be adjusted (reduced) based on the previous phase's outcome, thereby reducing the battery consumption of the overall system.

Moreover, the designed GPSJack system can be composed of up to 11 different nodes that collect data and communicate with a single tablet or SBC device. Based on the performed tests, the designed GPSJack (configured as shown in Figure 1, 5 nodes and 1 data aggregator) can communicate, without data loss, with the tablet device at a maximal distance of 45 m. Concerning the battery life, the GPSJack data collection nodes can compute for more than 48 h at a sampling frequency of 200 Hz.

Figure 9 presents an overview of the main characteristics of the GPSJack android mobile application running over a tablet device, presenting data collection and visualization.

**Figure 9.** GPSJack Android mobile application running on the tablet: node connection and data visualization.

The raw data (aka, time series), perceived by the GPSJack prototype (at a maximal sampling frequency of 200 Hz) and the existing in-laboratory architecture (perceived at a frequency of 200–1000 Hz), will be pre-treated by applying different data processing steps.

Finally, the GPSJack android mobile application executing on a tablet device has the ability to video-record at 60 FPS the performed tasks. Such recording is synchronized with the data stream perceived by the data collection nodes (i.e., Nordic Thingy 52).

#### 4.2.3. GPSJack/GPSEnv Synchronization

The GPSJack and the GPSEnv data streams present different timestamps and are not synchronized. The synchronization is manually performed offline, using the audio start signal emitted by the Vicon system and the video recording of the task performed by the GPSJack system. The annotator identifies the precise timestamp of the GPSJack system where the start audio signal is emitted by the GPSEnv system. In particular, the annotator identifies the precise video frame during which the signal is emitted (i.e., a granularity of 16 ms). In future developments, the aim will be to automatically synchronize the GPSJack/GPSEnv data streams using existing solutions [49], thereby excluding the time-consuming offline synchronization process.

#### *4.3. Data Analysis*

This section presents, starting with the data collected by the mentioned instruments (i.e., EMGs, force platform, kinematic, and GPSJack), the main data processing steps, performed with different processing methods in a defined order [50]. This workflow is presented in Figure 10.

**Figure 10.** Data analysis workflow.

#### 4.3.1. Data Cleaning

This phase emphasizes data patterns by reducing their dependence on environmental/HW noise and the data collection architecture, which often leads to data loss or corruption during transmission. In particular, dedicated data cleaning techniques must handle missing or corrupted data to maintain the time series structure and information. In this step, the corrupted and missing data issue is handled by applying an interpolation data-filling method that replaces such data with a value that follows the time series's previous and consequent pattern [51]. The noise's impact is reduced by applying a 4-order low-pass filter with a cut-off frequency of 20 Hz. We do not apply any data cleaning method to the existing in-lab architecture, since the architecture's proprietary software already performs such a step.

#### 4.3.2. Feature Extraction

Furthermore, since standard pattern-recognition models are not always suitable for raw data, the machine learning training phase is anticipated by a feature extraction step during which time-series are represented as a set of features in the time and/or frequency domain [52,53]. We make use of the https://github.com/fraunhoferportugal/tsfel accessed on 10 December 2022 [54] to represent each time window of 150 ms (equal to the APAs movement duration) perceived by the mentioned instrumentation in a set of 160 features in the frequency and time domains.

Table 1 shows the most commonly extracted time (e.g., min, max, mean, std, etc.) and frequency (e.g., Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), etc.) domain features that are usually combined, further increasing the recognition accuracy. Other features, extracted by using other frameworks or handcrafted (extracted manually), can be used, since the proposed workflow is modular. Thus, we can easily substitute each module.

**Table 1.** Most used time and frequency-domain features.

BUsers could also decide not to apply the feature extraction step and use the date in the form provided by the previous block applying a standard data segmentation phase. In such a case, the feature selection algorithm is not applied [13,50].

#### 4.3.3. Preprocessing of Features

The extracted features could present a wide range of values that will govern the training process, but such features are not those that primarily represent the dataset's characteristics or the final pattern-recognition model's accuracy. Data normalization transforms multi-scaled data to the same scale, and all variables equally influence the model, improving the learning algorithm's stability and performance [55]. Our methodology makes use

of the robust scaling normalization technique that scales each feature of the dataset by subtracting the median (*Q*2(*x*)) of this feature and then dividing by the interquartile range (IQR) (*Q*3(*x*) − *Q*1(*x*)). This scaler is robust to outliers, in contrast with the other scalers that arehighly affected by outliers. When working with datasets in which different features are used to represent every single sample, the datasets perform independent normalization for every feature.

Moreover, a large number of features does not imply high recognition quality, since they can positively or negatively impact the recognition process. Therefore, feature selection techniques identify features that positively and negatively influence the recognition process, reducing the model's dependence on irrelevant features. The exclusion of a certain number of features decreases the training process's complexity, since a smaller dataset generally requires less training time. The main benefits of such techniques are (i) reducing overfitting by eliminating redundant data, which consequently reduces noise-related issues; (ii) improving accuracy, since misleading data are eliminated; (iii) reducing training time due to fewer data points; and iv) raising interest in certain features demonstrating higher importance [55,56]. We use the tree-based feature selection technique to compute impurity-based feature importances, discarding irrelevant features in cooperation with other feature selection techniques.

In conclusion, the execution of this series of data treatment steps transforms the raw data, subject to noise and errors, to an optimal number of features in the time and frequency domains. This features set will be used by the machine learning models in the fourth phase of the methodology, as shown in Figure 3. The features-preprocessing step is anticipated by the hold/leave-out validation techniques, generating the training and testing datasets. Then, the training dataset will be preprocessed as mentioned above, and then the testing dataset is preprocessed based on the training dataset's requirements.

#### **5. Preliminary Experimental Evaluation**

Following the experimental design of Section 4, we collected data from 12 different male subjects, whose physical characteristic are shown in Table 2.

**Table 2.** Subjects' characteristics.

At the end of the data collection phase, each subject had performed a total of 84 7 tasks (i.e., do not rotate, rotate of an angle of 30◦ or 60◦ or 90◦ , both right and left) × 2 statuses (i.e., stable/unstable) × 6 repetitions) data collection sessions. Each session involved the five movements phases (i.e., rotate, in position, APAs, drop, and recover) described in Section 4.1 and shown in Figure 5.

In this preliminary evaluation, the collected data are divided based on the data collection technologies we utilized (force platform, EMGs, or acceleration data) into three different datasets, as shown in Table 3.

**Table 3.** Overview of the three types of datasets analyzed.

(# val) number of sensors.

Subsequently, for each dataset type (i.e., A, B, and C), we applied the data-processing pipeline defined in Section 4.3 and shown in Figure 10. In particular, the data, in segmentation and feature representation forms were segmented in time windows of 150 ms as the hypothetical duration of the APA movement phase), were used to train three different machine learning models, whose performances in recognizing the movement phases of Figure 5 were measured in terms of accuracy *A*), precision (*P*), recall (*R*), and F1-score (*F1*), defined as follows [57]:

$$A = \frac{tp + tn}{tp + tn + fp + fn} \qquad \qquad P = \frac{tp}{tp + fp}$$

$$R = \frac{tp}{tp + fn} \qquad \qquad F1 = 2 \times \frac{P \times R}{P + R}$$

Here, *tp* represents the number of true positives, *n* represents the number of true negatives, *fn* represents the number of false negatives, and *fp* represents the number of false positives.

#### *5.1. Preliminary Results*

Tables 4 and 5 present the results of our preliminary analysis. Table 4 presents the results for datasets A, B, and C from all subjects simultaneously by performing a k-fold test (k = 5) on all subjects' data. Table 5 presents the results for datasets A, B, and C for every single subject by performing a k-fold test (k = 5) on each subject's data.

**Table 4.** Average results for segmentation and feature-extraction data representation of all 12 subjects' data.

The results of Table 4 show that the random forest performed the best on all three datasets (i.e., A, B, and C), and for both data-treatment types (i.e., segmentation and feature extraction). Moreover, when differentiating by dataset type, the results show that the conjunct information of dataset A (i.e., EMGs and Force Platform) and dataset B (i.e., acceleration values) was put to use significantly better than when used separately. In particular, in terms of F1-score, there was an increment of 12.2% from dataset A to dataset C and 39.9% from dataset B to dataset C. Such results indicate that the acceleration provides precious information concerning the recognition of the studied activities. Overall, the achieved F1-scores for all subjects using the random forest model and dataset C were 88.2% and 89.6%, respectively, in segmentation and feature representation modes.

**Table 5.** Results on segmentation and feature extraction data representation for each subject.

Table 5 shows that when training and testing the models with one specific subject, the models' performances are subject-dependent. As shown from the statistics of each model for all subjects (bottom of the table), the RF model performed much better (F1 score > 91.6%) in both segmentation and feature representation modes, showing that the recognition accuracy, in terms of F1-score, is on average 5% higher than when training and testing with all 12 subjects' data (see Table 4). Again, differentiating by dataset type, the results show that the conjunct information of dataset A (i.e., EMGs and Force Platform) and dataset B (i.e., acceleration values) performed significantly better than when used separately. In particular, on average, there was an increment of 7.3% in the F1-score from dataset A to dataset C and 31.1% from dataset B to dataset C.

#### *5.2. GPSJack Evaluation*

Concerning the suitability and principal characteristics of the GPSJack system, this section presents its evaluation in terms of RAM, storage, CPU, battery consumption, and data

loss. In particular, a Samsung Galaxy Tab A7 with the following HW/SW characteristics has been tested in the setup described below.

Three Nordic Thingy 52 were connected to the Samsung Galaxy for three consecutive hours. The setup was tested once for each sampling frequency: (i) 50 Hz, (ii) 100 Hz, and (iii) 200 Hz. Table 6 presents the evaluation results obtained using the Android Studio Profiler suit.

**Table 6.** Data aggregators' profiling using Android Studio 4.1.2.

As observed, the designed system can work on various setups with no data loss and low storage, RAM, and CPU usage. Moreover, its battery consumption allows a data collection phase of almost 8 h. Based on the tests performed during the project, the Nordic Thingy 52 can efficiently compute for more than 48 consecutive hours at 200 Hz.

#### *5.3. Discussion*

The results of these preliminary experiments clearly show that the conjugate of EMG, Force Platform, and acceleration data performs considerably better than their separate utilization. This improvement enables a better understanding and in-depth study of human motion. In fact, the GPSJack prototype has good potential to capture relevant information, enabling the possibility to recognize the studied motion classes with an average F1-score of 89.6% when using all the subjects' data at once. Furthermore, when tested on single subjects, the F1-score ranged from a minimum of 83.2% to a maximum of 100%, outperforming the usage of only one of the aforementioned data sources. Even though the collected acceleration data present precious information, further work must be conducted to increase the overall performance and reduce the dependence on state-of-the-art technology. This can be done by: (i) implementing more complex recognition models than the used k-NN, RF, and LDA; and (ii) exploiting the utilization of a larger number and different positions of data collection nodes on the human body. Nevertheless, the information generated by the EMG and Force Platforms is paramount; thus, a possible next step in addition to those mentioned above would be the integration of EGM sensors into the same GPSJack nodes.

#### **6. Conclusions and Further Development**

This paper has dealt with defining the experimental design of the "Biofeedback Wearable and Environmental Technologies for Postural Correction" project. We illustrated the target technology, described the project's evaluation workflow (i.e., state-of-the-art instruments and low-cost wearable sensors, data processing flow, and machine learning-based analysis), and provided a high-level description of the context in which the envisioned technologies are forecasted to operate. In particular, we devised a methodology investigating how to build the parameters that allow the physiatric medical staff to evaluate the patient. Three challenging motor tasks were identified to, on the one hand, train fundamental motor skills such as action adaptation, compensation, and anticipation, and on the other hand, to measure the performance levels fir such skills. To measure the quality of the performed motor tasks, we evaluated a low-cost body area network (aka GPSJack). GPSJack uses at most eleven data collection nodes integrating an accelerometer, gyroscope, and magnetometer sensors and can compute for 48 h at a sampling frequency of 200 Hz. Moreover, we used an android mobile application that works as a data aggregator and controller for the GPSJack system, through which the user can observe the collected data and the posture and gait quality indicators. Nevertheless, in conjunction with the GPSJack, we integrated the GPSEnv based on state-of-the-art gait and posture evaluation systems.

Tests on data collected from 12 subjects for a total of 84 data collection sessions each showed that the designed system could highly accurately recognize the phases of the defined motor tasks. In particular, in a subject-independent setup, we achieved an F1-score of 89.6% in recognizing the five studied movement states (i.e., rotate, in position, APAs, drop, and recover). With a subject-dependent setup, the F1-score ranged from 100% for subjects 23 and 26 to 83.2% for subject 22. These results show that the acceleration information that we will add to the state-of-the-art systems significantly increases recognition capabilities.

It is widely accepted in the community of psychiatric medicine that proper quantification of the postural system's efficiency represents an essential assessment for improving quality of life in the elderly, patients with neurological pathologies, and athletes. Moreover, since most of the actual measurements are made in a laboratory environment where natural movements are constrained by the instruments applied to subjects' bodies and the environment, a system usable in uncontrolled and unconstrained environments (e.g., home, gym, or sports facilities) habilitates the individuals to move freely in their natural environment and perform the required motor tasks. Thus, the designed system will evaluate profiles from the performance viewpoint of individuals ranging from patients undergoing rehabilitation to top-level elite athletes in controlled and uncontrolled environments.

Since the final goal of the project is performing the defined task in uncontrolled environments and using only the acceleration information provided by a system such as GPSJack, the next step will concern the exploitation of the data collection phase while making use of a large number of nodes positioned on different body parts and of more complex pattern-recognition models than the used k-NN, RF, and LDA. In particular, deep learning models such as recurrent neural networks (RNN) and long short-term memory (LSTM) have shown optimal results in such fields. Moreover, since the information captured by the EMG sensors is paramount, integrating an EMG sensor into the GPSJack nodes should be considered.

**Author Contributions:** Conceptualization, P.C., G.P., P.M.P. and M.C.; methodology, P.C., G.P., F.D., P.M.P. and M.C.; software, T.C.W., F.D., C.T. (Claudio Tomazzoli), C.T. (Cristian Turetta) and F.P.; validation, T.C.W., F.D., L.Z., C.T. (Claudio Tomazzoli), C.T. (Cristian Turetta) and F.P.; formal analysis, P.C., G.P., F.D., P.M.P. and M.C.; investigation, All Authors; writing—original draft preparation, All authors; writing—review and editing, F.D., T.C.W., M.C., G.P. and P.C.; supervision, P.C., G.P. and M.C.; project administration, P.C., G.P., P.M.P. and M.C.; funding acquisition, P.C., G.P., P.M.P. and M.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Regione del Veneto grant number 1695-0007-1463-2019.

**Data Availability Statement:** Data are available at request

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Article* **An Efficient Machine Learning Approach for Diagnosing Parkinson's Disease by Utilizing Voice Features**

**Arti Rana <sup>1</sup> , Ankur Dumka 2,3, Rajesh Singh 4,5, Mamoon Rashid 6,7,\* , Nazir Ahmad <sup>8</sup> and Manoj Kumar Panda <sup>9</sup>**

**Abstract:** Parkinson's disease (PD) is a neurodegenerative disease that impacts the neural, physiological, and behavioral systems of the brain, in which mild variations in the initial phases of the disease make precise diagnosis difficult. The general symptoms of this disease are slow movements known as 'bradykinesia'. The symptoms of this disease appear in middle age and the severity increases as one gets older. One of the earliest signs of PD is a speech disorder. This research proposed the effectiveness of using supervised classification algorithms, such as support vector machine (SVM), naïve Bayes, k-nearest neighbor (K-NN), and artificial neural network (ANN) with the subjective disease where the proposed diagnosis method consists of feature selection based on the filter method, the wrapper method, and classification processes. Since just a few clinical test features would be required for the diagnosis, a method such as this might reduce the time and expense associated with PD screening. The suggested strategy was compared to PD diagnostic techniques previously put forward and well-known classifiers. The experimental outcomes show that the accuracy of SVM is 87.17%, naïve Bayes is 74.11%, ANN is 96.7%, and KNN is 87.17%, and it is concluded that the ANN is the most accurate one with the highest accuracy. The obtained results were compared with those of previous studies, and it has been observed that the proposed work offers comparable and better results.

**Keywords:** ANN; KNN; machine learning (ML); naïve Bayes classification; Parkinson's disease; SVM

#### **1. Introduction**

Parkinson's disease, commonly known as Tremor, is affected by a reduction in dopamine levels in the brain which damages a person's motion functions, or physical functioning. It is one of the world's most common diseases. Intermittent neurological signs and symptoms result from these lesions, which get worse as the disease progresses [1]. Because aging causes changes in our brains, such as loss of synaptic connections and changes in neurotransmitters and neurohormones, this condition is more frequent among the elderly.  The consequences of neurological problems and the falling dopamine levels in the patient's body show gradually, making them difficult to detect until the patient's condition requires medical treatment [2].

**Citation:** Rana, A.; Dumka, A.; Singh, R.; Rashid, M.; Ahmad, N.; Panda, M.K. An Efficient Machine Learning Approach for Diagnosing Parkinson's Disease by Utilizing Voice Features. *Electronics* **2022**, *11*, 3782. https://doi.org/10.3390/ electronics11223782

Academic Editors: Gabriella Olmo, Florenc Demrozi and Yu Zhang

Received: 13 October 2022 Accepted: 16 November 2022 Published: 17 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

However, the symptoms and severity levels are different for individuals. Major symptoms of this disease are deficiency in speech, short-term memory loss, loss of balance, and unbalanced posture [1].

Every year, 8.5 million individual cases of this disease are registered worldwide, as per the World Health Organization (WHO) report in 2019 [3]. The chance of developing this disease rises with age; currently, there are 4% of sufferers worldwide under 50 years of age. This disease is the most widespread neurodegenerative disease in the world after Alzheimer's disease, impacting millions of people [4,5]. Therapy for this disease is still in its initial stages, and doctors can only assist patients in alleviating the symptoms of the disease [6]. However, there are no definite diagnostics for this disease, and the diagnosis is largely dependent on the medical history of the patient [1]. As invasive procedures are typically used for diagnosis and therapy, which are both expensive and demanding [7], a reasonably straightforward and accurate way to diagnose this disease looks very relevant.

#### *1.1. Machine Learning-Based Detection of Parkinson's Disease*

Over the past few decades, researchers have looked at a new way of detecting this disease through ML techniques, a subset of artificial intelligence (AI). Clinical personnel might better recognize these disease patients by combining traditional diagnostic indications with ML.

As walking is the most common activity in every person's day-to-day life, it has been linked to physical as well as neurological disorders. This disease, for example, has been identifiable using gait (mobility) data. Gait analysis approaches offer advantages such as being non-intrusive and having the potential to be extensively used in residential settings [8]. Few researchers have attempted to combine ML methods to make the procedure autonomous and possible to do offline [9].

Furthermore, persons with the subject disease in its early stages might experience speech problems [10]. These include dysphonia (weak vocal fluency), repetitious echoes (a tiny assortment of audio variations), and hypophonia (vocal musculature disharmony) [7,11]. Information from human aural emissions might be detected and evaluated using a computing unit [12,13].

#### *1.2. Research Problem and Motivation*

Early PD detection in PD patients is a crucial challenge. Even if their health deteriorates, people can enhance their quality of life if they receive an early diagnosis. Another issue is that the diagnosis of PD requires a number of steps, including gathering a thorough neurological history from the patient and examining their motor abilities in various environments.

The majority of recent studies deal with the homo dataset (text, speech, video, or image). Problems with dataset modification and multi-data handling procedures have been highlighted in the suggested study. The effectiveness of disease prediction is regulated as a result of the examination of a particular dataset. More real-time solutions are made possible by the use of machine learning-based techniques for multivariate data processing. The multi-variate vocal data analysis (MVDA) is driven to provide multiple dataset attribute-based Parkinson's disease identification utilizing machine learning approaches. This study examines the potential for improving multi-variate and multimodal data processing, which aids in raising the disease detection rate. The existing research simultaneously concentrates on various ML-based techniques such as support vector machines, naïve Bayes, K-NN, and artificial neural network evaluations of Parkinson's data based on voice features. The MVDA employs extensive datasets and machine learning approaches to improve disease identification based on these works. The incorporation of numerous patients' multivariate acoustic characteristics in the proposed MVDA is encouraged. The subjective disease has been diagnosed with the help of proposed machine learning techniques under the MVDA system.

#### *1.3. Contribution*

This research article covers the techniques of machine learning which are implemented in the auditory analysis of speech to diagnose this disease. The benefits and shortcomings of these algorithms in detecting the disease are thoroughly contrasted, and existing

comparative studies' potential drawbacks are explored. The accuracy of ANN in speech analysis for diagnosis is the finest among different classifiers; however, the assumption is to enhance and adapt to the difficulties that may come from the data. Using the naïve Bayes classifier with suitable pre-processing might result in greater average accuracy. The main contribution of this paper is as follows:

#### *1.4. Structure of Proposed Work*

The structure of the study is as follows: Section 2 describes the related research survey. Section 3 discusses the methodology used to achieve the proposed objective. Section 4 defines the materials and methods. Section 5 examines the experiment and results. Section 6 discusses the comparative study and discussion. Finally, Section 7 concludes the proposed work.

#### **2. Related Works**

In order to distinguish PD cases from healthy controls, a variety of modern machine learning algorithms, including support vector machines, artificial neural networks, logistic regression, naïve Bayes, etc., have been successfully used. In this study, numerous databases, including Web of Science, Elsevier, MDPI, Scopus, Science Direct, IEEE Xplore, Springer, and Google Scholar, were utilized to survey relevant papers on Parkinson's disease.

In a survey by [14], the authors used KNN, SVM, and discrimination-function-based (DBF) classifiers for the diagnosis of PD. In their study, they used several parameters such as jitter, fundamental frequency, pitch, shimmer, and other statistical measures. The best accuracy among these classifiers was obtained from KNN with a 93.83% accuracy rate and it also provided good performance in other parameters, such as sensitivity, specificity, and error rate.

The authors in [15] used a convolution neural network classifier applied to speech classification datasets. The accuracy reached throughout the training phase, which was over 77%, makes the results optimistic. In accordance with the works mentioned above, [16] examined a variety of classifiers to identify individuals who were likely to have Parkinson's disease. They used 40 participants for their investigation, including 20 PD patients and 20 healthy controls. According to the experimental findings, the naive Bayes classifier has a detection accuracy of 65%, with a sensitivity rate of 63.6% and a specificity rate of 66.6%, respectively. In [17], the authors used three types of classifiers based on KNN, SVM, and multilayer perceptron (MLP) to diagnose Parkinson's disease. Among all these ML classifiers, SVM using an RBF kernel outperformed with an overall classification accuracy rate of 85.294%.

A summary of the most recent deep learning methods for audio signal processing is given in another work by [18]. The works that have been examined include convolution neural networks as well as other long short-term memory architecture models and audio-specific neural network models. Similar to the previous studies, [19] detected PD using naive Bayes and other machine learning approaches. In their method, relevant features were extracted from the voice signal of PD patients and healthy control subjects using signal processing techniques. The naive Bayes algorithm shows a 69.24% detection

accuracy and 96.02% precision rate for the 22 voice characteristics. In [20], the authors suggested a technique for detecting Parkinson's disease using SVM on shifted delta cepstral (SDC) and single frequency filtering cepstral coefficients (SFFCC) features extracted from speech signals of PD patients and healthy controls. Comparing the standard MFCC + SDC features to the SDC + SFFCC features, performance increases of 9% were observed. The 73.33% detection accuracy with a 73.32% F1-score was displayed by the conventional SVM on SDC + SFCC features. In addition to the naive Bayes classifier, several additional supervised methods, including but not restricted to well-known deep learning methods, have been suggested to identify PD patients among healthy controls.

In a survey conducted by [21], the authors examined two recognizing decision forests i.e., SysFor and ForestPA, along with the most widely used random forest classifier, which has been utilized as a Parkinson's detector. In their study, as compared to SysFor and ForestPA, random forest's average detection accuracy on incremental trees showed 93.58%. For the purpose of classifying Parkinson's disease through sets of acoustic vocal (voice) characteristics, the authors [22] suggested two frameworks based on CNN. Both frameworks are used for the mixing of different feature sets, although they combine feature sets in different ways. While the second framework provides feature sets to the parallel input levels that are directly connected to convolution layers, the first framework first combines several feature sets before passing them as inputs to the nine-layered CNN.

AI is assisting physicians in better diagnosing and treating diseases such as postoperative hypotension, and more advanced future models may have even more widespread medical uses. The evolutionary step in the creation of therapeutic pathways and adherence is machine learning. The real benefit of machine learning, however, is that it enables provider organizations to use information about the patient population from their own systems of record to create therapeutic pathways that are unique to their procedures, clientele, and physicians [23].

The vocal biomarkers and the description of the Aachen aphasia database, which contains recordings and transcriptions of therapy sessions, were covered in [24]. The authors also discussed how the biomarkers and the database could be used to build a recognition system that automatically maps pathological speech to aphasia type and severity.

In [25], the authors examined the suggested technique using a dataset of 288 audio files from 96 patients, including 48 healthy controls and 48 participants with cognitive impairment. The suggested method outperformed techniques based on manual transcription and speech annotation, with classification results that were comparable to those of the most advanced neuropsychological screening tests and an accuracy rate of 90.57%.

In [26], the authors intended to enlighten on the early indicators of major depressive relapse, which were discreetly measured using remote measurement technologies (RMT).

RMT has the potential to alter how depression and other long-term disorders are evaluated and handled if it is found to be acceptable to patients and other important stakeholders and capable of providing clinically meaningful information predicting future deterioration.

It can be seen from the reviews above that all the research that has been carried out is only restricted to a small number of datasets. The above previous works inspired us to try a new methodology. In this study, we experimented with several feature selection methods before comparing the results with various machine learning classifiers. Table 1 illustrates the review of ML techniques used to diagnose major symptoms of PD i.e., speech recording, handwriting pattern, and gait features, where data were collected from the UCI machine learning repository, the University of Oxford (UO), and other resources for 20 studies.

*Electronics* **2022**, *11*, 3782

**Table 1.** Comparative Studies of Machine Learning Approaches to diagnose Parkinson's Disease.

*Electronics* **2022**, *11*, 3782

#### **3. Proposed Work**

The proposed ML model uses an SVM, naïve Bayes, KNN, and ANN algorithm in the core. These algorithms are widely used in the literature since they are easy to use and only need a small number of parameters to be tuned. There are several processes involved in developing a model to detect PD from voice recordings. In the first phase, relevant features are extracted from the dataset for better understanding. In the second phase, machine learning techniques are applied to classify healthy as well as PD patients, which are dependent on acoustic features to predict the outputs in the form of visual representation of graphs and percentage of accuracy score tables. Finally, in the third phase, there is a difference between the entire machine learning classifier models to predict the best accuracy score. The complete technical process of the proposed work is represented in Figure 1. The proposed methodology is shown to be better than the other methodologies with respect to computational cost since few voice features were used instead of heavy feature extraction processes such as MRI, motion sensors, or handwriting assessments. Additionally, the performances of different popular classifiers were evaluated, and the best classifier was found to be ANN for PD diagnosis problems. *Electronics* **2022**, *11*, 3782 7 of 21 instead of heavy feature extraction processes such as MRI, motion sensors, or handwriting assessments. Additionally, the performances of different popular classifiers were evaluated, and the best classifier was found to be ANN for PD diagnosis problems.

Due to many available features, feature selection is a frequent approach used to minimize the dimension of data in machine learning based on voice analysis. As

redundancy and increasing relevance, which improves the accuracy of the disease's diagnosis. Prior to supplying the data to the classifier, a variety of feature selection strategies were used. The filter-based strategies take into account the importance of the characteristics. As a result, they are stable and scalable and have a low level of complexity [47,48]. The major drawback of this method is that, especially when the data are flowing in a stream, it may overlook certain useful aspects [49]. Both univariate and multivariate techniques based on filters are possible [50]. According to statistically based criteria such as information gain (IG) [51–53], the univariate approaches analyze attributes.

**Figure 1.** Diagram of the flowchart of the proposed work. **Figure 1.** Diagram of the flowchart of the proposed work.

*Feature Selection*

#### *Feature Selection*

Due to many available features, feature selection is a frequent approach used to minimize the dimension of data in machine learning based on voice analysis. As demonstrated in Figure 2, all feature selection algorithms have the same aim of reducing redundancy and increasing relevance, which improves the accuracy of the disease's diagnosis. Prior to supplying the data to the classifier, a variety of feature selection strategies were used. The filter-based strategies take into account the importance of the characteristics. As a result, they are stable and scalable and have a low level of complexity [47,48]. The major drawback of this method is that, especially when the data are flowing in a stream, it may overlook certain useful aspects [49]. Both univariate and multivariate techniques based on filters are possible [50]. According to statistically based criteria such as information gain (IG) [51–53], the univariate approaches analyze attributes. Multivariate approaches calculate feature dependence before ranking the feature. In addition, a widely utilized statistical technique for data analysis is principle component analysis (PCA). By choosing a collection of features that accurately reflects the entire data set, PCA can minimize the size of the data sets. The initial variables' principal components are the components with the largest variance value since PCA is a conversion technique. Following that, the other principal components are arranged in descending order of variance values [54]. Additionally, the wrapper-based algorithms assess the quality of the chosen features based on the learning classifier's performance. *Electronics* **2022**, *11*, 3782 8 of 21 Multivariate approaches calculate feature dependence before ranking the feature. In addition, a widely utilized statistical technique for data analysis is principle component analysis (PCA). By choosing a collection of features that accurately reflects the entire data set, PCA can minimize the size of the data sets. The initial variables' principal components are the components with the largest variance value since PCA is a conversion technique. Following that, the other principal components are arranged in descending order of variance values [54]. Additionally, the wrapper-based algorithms assess the quality of the chosen features based on the learning classifier's performance.

**Interpretation and Evaluation**

**Figure 2.** Feature Selection and Feature Extraction from Dataset. **Figure 2.** Feature Selection and Feature Extraction from Dataset.

In the pre-processing section, the whole procedure for filter techniques takes place independent of the model. The models are skipped by the filter. Filter methods primarily consider the data's distribution and correlation and internal relationships. As a result, filter techniques have the advantage of being simple and quick to compute. Because of their simplicity and quick computing speed, filter approaches are commonly used in the diagnosis of this disease. Some popular filtering methods are listed below. The minimum redundancy and maximum relevancy (mRMR) method selects characteristics that are far In the pre-processing section, the whole procedure for filter techniques takes place independent of the model. The models are skipped by the filter. Filter methods primarily consider the data's distribution and correlation and internal relationships. As a result, filter techniques have the advantage of being simple and quick to compute. Because of their simplicity and quick computing speed, filter approaches are commonly used in the diagnosis of this disease. Some popular filtering methods are listed below. The minimum redundancy and maximum relevancy (mRMR) method selects characteristics that are far apart but have a strong "correlation" with the classification variable.

apart but have a strong "correlation" with the classification variable. The wrapper method decides whether to have or reject a feature depending on a classifier's working change [55]. The wrapper method takes certain classifiers into account and provides a well-tailored subset. As a result, wrapper methods have a lower chance of finding the local maximum. Due to its huge gain in performance, the wrapper approach is popular among ML diagnostics. However, it has drawbacks such as being prone to overfitting and being computationally costly. Wrapper-based feature selection techniques use a classifier to build ML models with different predictor variables and select the The wrapper method decides whether to have or reject a feature depending on a classifier's working change [55]. The wrapper method takes certain classifiers into account and provides a well-tailored subset. As a result, wrapper methods have a lower chance of finding the local maximum. Due to its huge gain in performance, the wrapper approach is popular among ML diagnostics. However, it has drawbacks such as being prone to overfitting and being computationally costly. Wrapper-based feature selection techniques use a classifier to build ML models with different predictor variables and select the variable subset that leads to the best model.

In contrast, filter-based methods are statistical techniques independent of a learning algorithm used to compute the correlation between the predictor and independent

variable. The variables with higher scores are then used to build the ML model. Therefore, this research aims to use a filter-based feature selection method, to identify the most

variable subset that leads to the best model.

relevant features for improved PD detection.

In contrast, filter-based methods are statistical techniques independent of a learning algorithm used to compute the correlation between the predictor and independent variables. The predictor variables are scored according to their relevance to the target variable. The variables with higher scores are then used to build the ML model. Therefore, this research aims to use a filter-based feature selection method, to identify the most relevant features for improved PD detection.

#### **4. Materials and Methods**

#### *4.1. Dataset*

The dataset of recorded speech signals was obtained from Max Little of the University of Oxford [56,57]. Table 2 contains the details of the dataset. This dataset has an assortment of acoustic speech measures from 195 persons, where 147 persons have Parkinson's disease. All the attributes in the dataset characterize an individual voice measure, and each tuple represents a total number of voice recordings made by these people. The objective of the dataset is to differentiate fit persons compared to the unhealthy using the "status" column, which is set to negative for fit persons and positive for those having the disease.

**Table 2.** Detail of Parkinson's Dataset.

#### *4.2. Parkinson's Disease Diagnosis Based on Voice Analysis and Machine Learning*

Some studies have concentrated on the acoustic level or the fluctuations in fundamental frequency (F0) caused by vocal activities. The effects of power spectral analysis of F0 phonation in persons with sensorineural audibility loss and the disease have been examined in [58–60]. F0's rhythm was unique in the incidence and amplitude of the diseases. Further, the study demonstrated that the F0 analysis can be a useful tool for neurological diseases under investigation. The autocorrelation function approach was used to find the basic frequencies of speech transmissions. According to the concept, Parkinsonian dysprosody is frequently described as a simple neuro-motor disorder.

The understanding and generation of pitch characteristics in a group of patients were examined to confirm the idea. Conventional medications, such as LDOPA, define that in the early stages of PD, LDOPA is a very effective treatment of subjective disease [61]. In [62], the authors use deep learning to categorize the patient's speech data as "severe" and "not severe". The evaluation measures employed in this study were the unified Parkinson's disease ranking scale (UPDRS). The motor UPDRS examines the patient's motor ability on a 0–108 scale, while the entire UPDRS provides a range of scores from 0 to 1766.

#### *4.3. Classification of Parkinson's Disease with ML Classifier*

In this technique, we'll use an ML classifier to classify the disease. First, we select a target variable of patient health status and measure the number of patients in this report. We visualize the data graphically after assessing the health status of a patient. Two types of datasets were developed: 80% of the dataset was used for training and 20% for the testing dataset. In the following Figure 3, the score of 0 represents the healthy persons in the sample, whose count is 48, and 1 represents the patients with Parkinson's disease,

*Electronics* **2022**, *11*, 3782 10 of 21

whose count is 147. The count of Parkinson's disease patients in the dataset: 147 out of 195 (75.38%). The count of healthy persons in the dataset: 48 out of 195 (24.62%). *Electronics* **2022**, *11*, 3782 10 of 21 **Healthy**

#### *4.4. Building of Machine Learning Techniques with Classifier Evaluation Metrics 4.4. Building of Machine Learning Techniques with Classifier Evaluation Metrics* By using different types of classifiers, it becomes easy to detect the disease.

By using different types of classifiers, it becomes easy to detect the disease. Classification sensitivity, Matthews's correlation coefficient (MCC), accuracy, specificity, F-score (F-measure), and other measurement parameters are used to distinguish it. Each of these measurement criteria includes a formula for calculating it and determining which classifier is the most qualitatively appropriate for the analysis. It is requisite to focus on the confusion matrix before developing these criteria [63]. The confusion matrix of the multi-class classifier is shown in Figure 4. By using different types of classifiers, it becomes easy to detect the disease. Classification sensitivity, Matthews's correlation coefficient (MCC), accuracy, specificity, F-score (F-measure), and other measurement parameters are used to distinguish it. Each of these measurement criteria includes a formula for calculating it and determining which classifier is the most qualitatively appropriate for the analysis. It is requisite to focus on the confusion matrix before developing these criteria [63]. The confusion matrix of the multi-class classifier is shown in Figure 4. Classification sensitivity, Matthews's correlation coefficient (MCC), accuracy, specificity, F-score (F-measure), and other measurement parameters are used to distinguish it. Each of these measurement criteria includes a formula for calculating it and determining which classifier is the most qualitatively appropriate for the analysis. It is requisite to focus on the confusion matrix before developing these criteria [63]. The confusion matrix of the multi-class classifier is shown in Figure 4.

#### **Predicated Class Predicated Class**

F1-Score: It represents the accuracy of a model on a given dataset which is also known as F-Score as shown in Equation (1): **Figure 4.** Confusion Matrix with Sensitivity, Specificity, Accuracy, and Precision value. **Figure 4.** Confusion Matrix with Sensitivity, Specificity, Accuracy, and Precision value.

− = 2 <sup>∗</sup> <sup>∗</sup>

MCC: It is utilized for model evaluation to evaluate the quality of the binary and

multi-class classifications as shown in Equation (2). It is based on true-negative, true-

F1-Score: It represents the accuracy of a model on a given dataset which is also known

MCC: It is utilized for model evaluation to evaluate the quality of the binary and

<sup>+</sup> (1)

<sup>+</sup> (1)

as F-Score as shown in Equation (1):

F1-Score: It represents the accuracy of a model on a given dataset which is also known as F-Score as shown in Equation (1):

$$F-Score = \mathcal{2} \* \frac{precision \* Sensitivity}{precision + Sensitivity} \tag{1}$$

MCC: It is utilized for model evaluation to evaluate the quality of the binary and multiclass classifications as shown in Equation (2). It is based on true-negative, true-positive, and false-negative, false-positive. It lies between −1 to 1 which is defined as follows:

$$\text{MCC} = \frac{TP \ast TN - FP \ast FN}{\sqrt{(TP + FP)(TP + FN)(TN + FP)(TN + FN)}} \tag{2}$$

(−1): Contradiction between prediction and observation

(0): No better than random prediction

(1): Perfect classifier (accurate prediction).

#### **5. Experiments and Results**

The proposed work is implemented in Python 3.7: JupyterLab. Here we detail the experimental setup and the results of the four machine learning classification methods.

#### *5.1. SVM-Classifier*

SVM is one of the most prevalent classifier models because it provides accurate as well as highly robust results. The fundamental goal of SVM is to classify the training data by separating the classes while executing a multiple-class learning activity. It allows for the best classification performance on training data and accurately classifies patterns from the data [64]. The training procedure uses a sequential minimization strategy, and classification accuracy is shown to be higher in SVM due to its greater generalization ability [65]. The linear SVM is calculated by using the following Equation (3).

$$y = f(\mathbf{x}) = w^T \mathbf{x} - b \tag{3}$$

where *x* represents the data, *y* represents the class label, *w* represents the weight of vector orthogonal to the decision hyper-plane, *b* represents the offset of the hyper-plane and *T* shows the transpose operator [66].

In this study, we use the sklearn library in the SVM-classifier module for the classification of the given dataset. Table 3 represents the results that are generated by using the SVM classifier (Figure 5). Figure 6 represents the confusion matrix with the true positive, true negative, false positive, and false negative value of a PD person by using the SVM classifier.

**Table 3.** SVM Classifier.

True Healthy

*Electronics* **2022**, *11*, 3782 12 of 21

**Figure 5.** Results obtained by SVM. **Figure 5.** Results obtained by SVM.

**Figure 6.** Confusion Matrix and Heatmap of SVM Classifier. **Figure 6.** Confusion Matrix and Heatmap of SVM Classifier.

#### *5.2. Naive Bayes Classifier*

**Figure 6.** Confusion Matrix and Heatmap of SVM Classifier. *5.2. Naive Bayes Classifier* Another main essential category method of ML is the naive Bayes classifier technique. It provides effective classification and learning and the majority of results are acquired through the naïve Bayes method [67]. Naïve Bayes, based on Bayes' theorem, determines the likelihood of an event occurring depending on the event's circumstances. For instance, variations in the voice are common in people with the disease; hence, these symptoms are linked to the prediction for diagnosis of this disease. The naive variation of *5.2. Naive Bayes Classifier* Another main essential category method of ML is the naive Bayes classifier technique. It provides effective classification and learning and the majority of results are acquired through the naïve Bayes method [67]. Naïve Bayes, based on Bayes' theorem, determines the likelihood of an event occurring depending on the event's circumstances. For instance, variations in the voice are common in people with the disease; hence, these symptoms are linked to the prediction for diagnosis of this disease. The naive variation of the theorem extends and simplifies the original Bayes theorem, which gives a mechanism for determining the probability of a target occurrence. To estimate the likelihood of the medical condition, the data comprise numerous speech signal variants. The sklearn Gaussian naive Bayes algorithm is used to provide the classifier module for the execution of the naïve Bayes categorization. The result of the classifier is shown in Table 4 and Another main essential category method of ML is the naive Bayes classifier technique. It provides effective classification and learning and the majority of results are acquired through the naïve Bayes method [67]. Naïve Bayes, based on Bayes' theorem, determines the likelihood of an event occurring depending on the event's circumstances. For instance, variations in the voice are common in people with the disease; hence, these symptoms are linked to the prediction for diagnosis of this disease. The naive variation of the theorem extends and simplifies the original Bayes theorem, which gives a mechanism for determining the probability of a target occurrence. To estimate the likelihood of the medical condition, the data comprise numerous speech signal variants. The sklearn Gaussian naive Bayes algorithm is used to provide the classifier module for the execution of the naïve Bayes categorization. The result of the classifier is shown in Table 4 and graphical representation is illustrated in Figure 7.

0 1

graphical representation is illustrated in Figure 7.

the theorem extends and simplifies the original Bayes theorem, which gives a mechanism for determining the probability of a target occurrence. To estimate the likelihood of the medical condition, the data comprise numerous speech signal variants. The sklearn Gaussian naive Bayes algorithm is used to provide the classifier module for the execution of the naïve Bayes categorization. The result of the classifier is shown in Table 4 and

**Table 4.** Naïve Bayes Classifier Results.

*Electronics* **2022**, *11*, 3782 13 of 21

**Figure 7.** Results obtained by Naïve Bayes. **Figure 7.** Results obtained by Naïve Bayes.

#### *5.3. Artificial Neural Network 5.3. Artificial Neural Network*

ANN is a subfield of deep neural networks that predict how the human brain works. In general, there is a significant distinction between the human brain and ANN. The brain has 'n' number of parallel neurons, whereas the machine only has a finite sum of processors. Additionally, neurons are meeker and more relaxed than computer processors. Another major disparity between computer systems and the brain is the ability to process information on a larger scale. Neurons are made up of synapses or networks that operate together [64,68]. In this article, the main aim is to classify the functionality of ANN techniques in the early detection of this disease which is built on the subsequent ANN is a subfield of deep neural networks that predict how the human brain works. In general, there is a significant distinction between the human brain and ANN. The brain has 'n' number of parallel neurons, whereas the machine only has a finite sum of processors. Additionally, neurons are meeker and more relaxed than computer processors. Another major disparity between computer systems and the brain is the ability to process information on a larger scale. Neurons are made up of synapses or networks that operate together [64,68]. In this article, the main aim is to classify the functionality of ANN techniques in the early detection of this disease which is built on the subsequent phases:

iv. Examining the accurate outcomes. These outcomes can be further used in the medical sector as direction for developers considering ANN deployment to enhance the civic health potential as a reaction to the These outcomes can be further used in the medical sector as direction for developers considering ANN deployment to enhance the civic health potential as a reaction to the studied disease [69].

studied disease [69]. In the experiment of an artificial neural network, the dataset was split into two parts i.e., the training dataset (80%) and the test dataset (20%). The classification results of the artificial neural network were found to be very high in the form of the average accuracy score which was the highest among all the classification methods, i.e., 96.7% shown in In the experiment of an artificial neural network, the dataset was split into two parts i.e., the training dataset (80%) and the test dataset (20%). The classification results of the artificial neural network were found to be very high in the form of the average accuracy score which was the highest among all the classification methods, i.e., 96.7% shown in Table 5 and graphical representation is shown in Figure 8.

Table 5 and graphical representation is shown in Figure 8.

**Figure 8.** Results obtained by ANN. **Figure 8.** Results obtained by ANN.

#### *5.4. K-Nearest Neighbor 5.4. K-Nearest Neighbor*

The KNN technique is costly while presenting with a huge training dataset since it has been used most of the time in pattern recognition. KNN is the base concept of learning by analogy utilized to categorize the nearest neighbors. It is accomplished by comparing closely similar training tuples to the provided test tuple. As a result, "n" characteristics are utilized to recognize training tuples in which each tuple corresponds to a distinct point in the n-dimensional space. The KNN classifier's responsibility in the event of an unlabeled tuple is to explore the pattern space for all k training tuples that are close together [64]. This study aims to identify the accuracy rate of detecting the subject disease. To find out the difference between affected patients and healthy persons, the KNN algorithm is used. In terms of accuracy, experimental data reveal that the ANN classifier outperformed the KNN classifier on average. The results of the KNN classifier are shown The KNN technique is costly while presenting with a huge training dataset since it has been used most of the time in pattern recognition. KNN is the base concept of learning by analogy utilized to categorize the nearest neighbors. It is accomplished by comparing closely similar training tuples to the provided test tuple. As a result, "n" characteristics are utilized to recognize training tuples in which each tuple corresponds to a distinct point in the n-dimensional space. The KNN classifier's responsibility in the event of an unlabeled tuple is to explore the pattern space for all k training tuples that are close together [64]. This study aims to identify the accuracy rate of detecting the subject disease. To find out the difference between affected patients and healthy persons, the KNN algorithm is used. In terms of accuracy, experimental data reveal that the ANN classifier outperformed the KNN classifier on average. The results of the KNN classifier are shown in Table 6 with the accuracy rate of the training and test datasets, F1-score, and MCC illustrated in Figure 9.

in Table 6 with the accuracy rate of the training and test datasets, F1-score, and MCC illustrated in Figure 9. **Table 6.** KNN Classifier Results.

F1-score 71% MCC 65.02%

**Figure 9.** Results obtained by KNN. **Figure 9.** Results obtained by KNN.

#### *5.5. Summary of Evaluation Results 5.5. Summary of Evaluation Results*

The performance of all the classifier models used in the experiment for the disease's prediction is illustrated in Table 7. The artificial neural network classifier scores the highest accuracy rate followed by SVM, naïve Bayes, and KNN. Figure 10 shows the graphical representation of the results obtained by these four ML classifiers based on various parameters. Table 7 illustrates that SVM attained the average accuracy for the training and test datasets, which are 88.46% and 87.17% respectively, F1-score (66.19%), and MCC (56.59%), sensitivity and specificity 62.5% and 93.54%, respectively. In addition, The performance of all the classifier models used in the experiment for the disease's prediction is illustrated in Table 7. The artificial neural network classifier scores the highest accuracy rate followed by SVM, naïve Bayes, and KNN. Figure 10 shows the graphical representation of the results obtained by these four ML classifiers based on various parameters. Table 7 illustrates that SVM attained the average accuracy for the training and test datasets, which are 88.46% and 87.17% respectively, F1-score (66.19%), and MCC (56.59%), sensitivity and specificity 62.5% and 93.54%, respectively. In addition, the naïve Bayes achieved the average accuracy for the training and test datasets, F1-score, MCC, sensitivity, and specificity, which are 76.23%, 74.11%, 86.74%, 66.56%, 84%, and 79.76% respectively.

MCC, sensitivity, and specificity, which are 76.23%, 74.11%, 86.74%, 66.56%, 84%, and **Table 7.** An overview of evaluation results.

the naïve Bayes achieved the average accuracy for the training and test datasets, F1-score,

SVM 88.46% 87.17% 66.19% 56.59% 62.5% 93.54% Naïve Bayes 76.23% 74.11% 86.74% 66.56% 84% 79.76% KNN 88.46% 87.17% 71% 65.02% 60.0% 93.54% ANN 97.4% 96.7% 87.01% 70.11% 92.42% 91.25% It has been observed that the results obtained by the SVM and KNN have the same values for all the parameters except MCC (65.02 %) and sensitivity (60%). Finally, the best accuracy was obtained by the ANN where the results of parameters such as accuracy of the training and test datasets, F1-score, MCC, sensitivity, and specificity are 97.4%, 96.7%, 64.55%, 87.01%, 70.11%, 92.42%, and 91.25%, respectively. Overall, the results of our experiments show that ANN outperforms SVM, naive Bayes, and KNN.

It has been observed that the results obtained by the SVM and KNN have the same values for all the parameters except MCC (65.02 %) and sensitivity (60%). Finally, the best accuracy was obtained by the ANN where the results of parameters such as accuracy of the training and test datasets, F1-score, MCC, sensitivity, and specificity are 97.4%, 96.7%,

experiments show that ANN outperforms SVM, naive Bayes, and KNN.

#### **6. Comparative Study and Discussion 6. Comparative Study and Discussion**

This section examines the efficient comparative result analysis of the proposed technique with other conventional machine learning techniques. The comparison of the proposed study with previously published research is shown in Table 8. This section examines the efficient comparative result analysis of the proposed technique with other conventional machine learning techniques. The comparison of the proposed study with previously published research is shown in Table 8.

Mabrouk et al.,[73] Speech Random forest, SVM, MLP, KNN 78.4% (SVM), 82.2% (KNN) NA NA Benba et al. [74] Speech HFCC-SVM 87.5% 90% 85% **Proposed Work Speech SVM, naïve Bayes, KNN and ANN 87.17%, 74.11%, 87.17%, and 96.7% 62.5%, 84%, 60%, and 92.42% 93.54%, 79.76%, 93.54%, and 91.25%** As per the comparative analysis, the proposed model (using four machine learning algorithms) shows better results obtained as compared to all other experimental machine learning models and the existing state of the art. In the proposed study, the best result was achieved by ANN with 96.7% accuracy, which is higher than the other experimental algorithms. The authors of [49] collected 20 PD and 20 HC speech datasets using high-quality recording equipment and used KNN and SVM to analyze the datasets in order to detect PD. KNN and SVM classifiers performed with accuracy rates of 59.52% (LOSO) and 68.45% (LOSO), respectively. In addition to [50], the authors used various algorithms such as C4.5, C5.0, random forest, and CART based on decision trees. The authors experimented on 40 individuals' records, where 50% were affected with the subjective disease and 50% were HC. For this study, the highest average model accuracy of 66.5% was attained. ANN was used

by [51] to identify PD. The dataset was obtained from the University of California, Irvine's machine learning library. A total of 45 attributes were chosen as input values and one outcome for the categorization using the MATLAB tool. With an accuracy of 94.93%, their suggested model was able to differentiate healthy individuals from PD subjects. In [52], the authors used random forest, SVM, MLP, and KNN classifiers for the detection of PD patients from HC. The result obtained from this study was 78.4% and 82.2% for the SVM and KNN classifiers, respectively. In a study by [53], the authors examined the comparison between the patients with PD (PWP) and healthy controls (HC) based on a variety of speech samples. In their study, human factor cepstral coefficients (HFCC) were applied. The extracted HFCC was used to generate the average voice print for each voice recording. For the classification, SVM was used with a variety of kernels, including RBF, polynomial, linear, and MLP. The SVM's linear kernel allowed for the highest accuracy of 87.5%.

In addition to the comparisons mentioned above, the performance of the proposed methodology is compared with related ML methods for PD analysis in various scenarios and with various types of evaluated PD datasets. The proposed technique outperformed other similar contributions of ML methods in terms of performance for diagnosing PD, as seen in the above table, and is thus superior to them.

#### **7. Conclusions**

Automated ML techniques may classify PD from HC and predict the outcome using non-invasive speech biomarkers as features. With noisy and high-dimensional data, our study compares the performance of multiple machine learning classifiers for disease detection. Accuracy at the clinical level is feasible with careful feature selection. In this paper, we compared ML classifiers: SVM with an accuracy of 87.17%, naïve Bayes' classifier with an accuracy of 74.11%, ANN with an accuracy of 96.7%, and KNN with an accuracy of 87.17%. We used these techniques to distinguish between affected patients and healthy people. The disease is diagnosed using human speech signals. The acquired results demonstrate how feature selection techniques work well with ML classifiers, especially when working with voice data where it is possible to extract a large number of phonetic characteristics. The proposed early diagnosis approach makes it possible to detect PD with high accuracy in its early stages and the subjective disease's severe symptoms can be prevented. Many categorization algorithms are being used in the medical imaging area to obtain the best level of accuracy. This research may be used in different machine learning methods and datasets to improve classifier performance and reach the maximum accuracy score. In order to improve the accuracy of the models created, future efforts will make use of the alreadyexisting recordings and add to the number of existing attributes. In order to compare the collected data, various different records processing software that are available online may also be used.

**Author Contributions:** Conceptualization, A.R.; methodology, A.R. and A.D.; validation, A.R. and M.R.; formal analysis, A.R. and N.A.; writing—original draft preparation, A.R.; writing—review and editing, M.K.P. and M.R.; supervision, A.D. and R.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** There was no external funding received for this article.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data in this research paper will be shared upon request made to the first author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Predicting Alzheimer's Disease Using Deep Neuro-Functional Networks with Resting-State fMRI**

**Sambath Kumar Sethuraman <sup>1</sup> , Nandhini Malaiyappan <sup>2</sup> , Rajakumar Ramalingam <sup>3</sup> , Shakila Basheer <sup>4</sup> , Mamoon Rashid 5,6,\* and Nazir Ahmad <sup>7</sup>**

**Abstract:** Resting-state functional connectivity has been widely used for the past few years to forecast Alzheimer's disease (AD). However, the conventional correlation calculation does not consider different frequency band features that may hold the brain atrophies' original functional connectivity relationships. Previous works focuses on low-order neurodynamics and precisely manipulates the mono-band frequency span of resting-state functional magnetic imaging (rs-fMRI). They specifically use the mono-band frequency span of rs-fMRI, leaving out the high-order neurodynamics. By creating a high-order neuro-dynamic functional network employing several levels of rs-fMRI timeseries data, such as slow4, slow5, and full-band ranges of (0.027 to 0.08 Hz), (0.01 to 0.027 Hz), and (0.01 to 0.08 Hz), we suggest an automated AD diagnosis system to address these challenges. It combines multiple customized deep learning models to provide unbiased evaluation, and a tenfold cross-validation is observed We have determined that to differentiate AD disorders from NC, the entire band ranges and slow4 and slow5, referred to as higher and lower frequency band approaches, are applied. The first method uses the SVM and KNN to deal with AD diseases. The second method uses the customized Alexnet and Inception blocks with rs-fMRI datasets from the ADNI organizations. We also tested the other machine learning and deep learning approaches by modifying various parameters and attained good accuracy levels. Our proposed model achieves good performance using three bands without any external feature selection. The results show that our system performance of accuracy (96.61%)/AUC (0.9663) is achieved in differentiating the AD subjects from normal controls. Furthermore, the good accuracies in classifying multiple stages of AD show the potentiality of our method for the clinical value of AD prediction.

**Keywords:** rs-fMRI; classifications; high-order neuro-dynamic functional network; deep learning; Alzheimer's disease

#### **1.  Classified as the typical cause of dementia, it comprises memory loss, loss of spatial orientation, lack of time sense, behavioral issues and, at the acute stages, retrograde amnesia and mild cognitive impairment [3,4]. The disease is characterized by the unique "clumps" found in the brains of patients, termed medically as amyloid plaques and tangled

**Citation:** Sethuraman, S.K.; Malaiyappan, N.; Ramalingam, R.; Basheer, S.; Rashid, M.; Ahmad, N. Predicting Alzheimer's Disease Using Deep Neuro-Functional Networks with Resting-State fMRI. *Electronics* **2023**, *12*, 1031. https://doi.org/10.3390/ electronics12041031

Academic Editors: Alberto Fernandez Hilario and Gabriella Olmo

Received: 24 December 2022 Revised: 16 February 2023 Accepted: 17 February 2023 Published: 19 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

fibers called neurofibrillary tangles. As the ailment progresses, the above-listed anomalies in the brain result in the degradation of the neural networks, causing the gradual loss of bodily functions. The progression from the loss of mental capabilities to physical degradation renders AD physically and mentally taxing to the families of the afflicted and the caregivers. Recent advancements in medical care have increased life expectancy, which in turn has increased the aged population [5]. Thus, the fraction of the human population susceptible to AD has also increased. Some researchers have begun using computer techniques such as neural networks, optimization, machine learning, and so on to solve the medical domain issues [6].

In existing ML techniques, a field expert manually extracts and labels features. Especially in the field of computer vision, deep learning (DL), an advanced machine learning (ML) technique, outperforms classical ML in terms of detecting inclusive structures in complex, high-dimensional data. The main benefit of DL algorithms is that they attempt to incrementally learn high-level properties from the brain imaging data, minimizing the need for domain expertise. DL outperforms ML since it can accurately handle enormous volumes of data, while ML algorithms require a specific processing step.

#### *Objectives of the Study*

Automated diagnosis systems have gained importance in the field of medical image analysis. The recurring patterns in images have the potential to determine the conformation, function, and activities of the brain. Unlike most popular AD discovery algorithms, the input dataset is extensive. Therefore, an efficient technique is essential.

The main objective of this research work is to propose an automated AD diagnosis system by developing a high-order neuro-dynamic functional network. LFOs (Low-Frequency Oscillations) also refer to slow brain activity fluctuations between 0.01 to 0.08 Hz. To understand brain atrophies, these slow fluctuations are analyzed using different levels, such as slow4, slow5, and full-band ranges. The use of LFOs in brain studies allows for examining slow changes in brain activity that may be relevant to various neurological conditions.

#### **2. Related Works**

Many researchers are interested in revisiting this area to identify a treatment for AD because of the relevance of early detection of the disease. Therefore, the most significant studies in this area will be presented in this section. The classified approach of the MCI (mild cognitive impairment) and AD patients using different network approaches with strengths and weaknesses are described. Ting Ma et al. [7] have extracted two important parameters constructed via the pre-processed data of rs-fMRI, such as ALFF and ReHo. In addition, their findings imply that during deterioration, ROIs in the brain may experience various physiological alterations. Evanthia E. Tripoliti et al. [8] created the five phases of the method by including preprocessing fMRI to remove non-task-related variability, modelling the BOLD material resulting in the stimulus, extracting from fMRI image data, features selection, and finally, the random forest algorithm. The methods assist in classifying the disease, with 80.5–87% accuracy. Dachena et al. [9] elaborated on MRI and fMRI shared with the misuse of MMSE to discriminate AD using SVM classification. Additionally, the multimodal approach (MRI, fMRI, MMSE) provides more accuracy of 95.65% and specificity of 97.22% with a sensitivity of 93.39%. Zhe wang Li et al. [10] classified AD, MCI, and NC and proposed a regularized LDA approach that reduces the noise effect by using two required shrinkage methods. Furthermore, they investigated the relationship between LDA and Maximum Likelihood-based classifications. These developed methods can be applied to a limited sample size.

Babajani-Feremi et al. [11] have developed an approach that can discriminate possible MCI-decliners using structural and functional MRI integration for AD identification. A multi-scale time series kernel-based learning model was used to diagnose brain diseases as the foundation for the traditional statistical analysis technique proposed by Fei Guo et al. [12]. They found that this method has advantages for accurately identifying

brain diseases. Xia-an Bi et al. [13] classified AD, patients' abnormal brain regions, and HC by proposing a random neural network cluster based on fMRI data and found that a neural network cluster is a suitable approach for identifying AD. His group also integrates other imaging to know brain activity and combines brain and cerebellum activity. In addition, they differentiate AD and HC patients. In this study, the authors examined 138 participants using various accuracy criteria.

The model for early-stage detection from functional alterations in MRI images was created by Modupe Odusami et al. [14] using ResNet18. The accuracy of the ResNet18 is as follows: the attained results are 99.9% for EMCI (Early Mild Cognitive Impairment) against AD, 99.95% for LMCI (Late Mild Cognitive Impairment) against AD, and 99.95% for MCI against EMCI. Accuracy, sensitivity, and specificity were all improved using the created model. Then, a novel three-dimensional two stage-age-network (TSAN) was used to compute brain age using T1-weighted MRI data. The two-stage network design used by TSAN was demonstrated [15]. (i) The first stage network more accurately calculates the approximate brain age from the discretized brain age, and (ii) the first stage network measures brain age. Additionally, some researchers used machine learning methods to categorize AD. Feature extraction from ADNI's fMRI images was used in this work [16], and the performance analysis is based on the confusion matrix. Additionally, the author has developed various techniques for CNN architecture and machine learning classifiers (SVM, KNN, DT, RF, and LDA). The accuracy levels provided by the suggested model are 85.8%, 77.5%, 91.7%, 96.7%, and 79.5%. Finally, the accuracy levels provided by the CNN architecture are 98.1%, 95.2%, 87.5%, and 89.0%, respectively.

Quamzheng Li et al. [17] built the R-fMRI data to calculate the functional connectivity of different brain areas. Additionally, a standard control-targeted autoencoder network was constructed to distinguish between MCI and normal ageing. The technique offers accurate AD classifiers and discriminative brain network characteristics. Deep learning outperforms the more traditional R-fMRI method in categorizing high-dimensional multimedia data, as shown by the accuracy increases of 31.21%.

Unmang Gupta et al. [18] presented an architecture that operates by using the 2D-CNN model to encode each 2D slice of the MRI. It shows that when compared to the most cuttingedge methods, the permutation invariant layers train more quickly and produce better predictions. Additionally, they provide more accurate estimates of healthy participants' brain ages. Cross-validation of the sMRI-fMRI model by Vince D. Calhoun et al. [19] indicates that it performed better than a unimodal prediction analysis. Additionally, some research is based on data from the correlation coefficients between the R-fMRI signal and functional intellectual network creation. Compared to the former method, this method demonstrated an increased diagnostic accuracy of about 25%. The convolutional component of the Spatial-Temporal Net is employed to describe the spatial dependency between the time series segments of various brain areas and to predict the course of AD using rs-fMRI time series data. This method performed better than the most recent methodology in terms of categorization accuracy. Furthermore, it sheds light on the pathogenic chain that underlies AD [20].

Based on an examination of fMRI data, Yifei Zhang et al. [21] explains a unique technique for differentiating AD patients from normal (healthy) individuals: functional connectivity between the brain's activity voxels. The predicted AD patients are significantly influenced by the FC between activity voxels inside the prefrontal lobe and those between the prefrontal and parietal lobes, according to the suggested technique, which demonstrated higher classification accuracy. It also has a high prospective value.

Uttam Khatri et al. [22] examined the dynamic frequency functional networks at frequency response time series, including full-band, slow-4, and slow-5 bands, using the rs-fMRI data amassed by the ADNI. His team also combined four frequency bands with dynamic frequency brain functional network elements to aid in the early identification of AD. In addition, it also offers a fresh perspective on how the brain network functions and offers early Alzheimer's detection. The author also attained a 94.10% classification accuracy level, 96.75% specificity, and 90.95% sensitivity. The High-Order Dynamic Functional Connectivity model's experimental results can improve the classification performance with different levels of evaluation matrices to identify the AD.

The author in [23] implemented two significant approaches—first, normal CNN methods with 2D and 3D structural brain images. Second, transfer learning methods were applied achieved 97%. Deep learning methods reached 95.17% and 93.61% accuracy for 3D and 2D multiclass AD and MCI classifications. The authors in [24] incorporated unsupervised convolutional spiking neural networks trained with the preprocessed ADNI datasets. They achieved three binary categories without the spike of 86.90%, 83.25%, and 76.70%.

#### *The Motivation for Study*

However, all the existing methods have their bottlenecks and limitations. Therefore, the model aims to establish a novel DL method that can push the classification accuracy boundaries towards the most accurate AD and MCI classification approaches. This research model leads to finding out the limitations of an early diagnosis of AD.

So, with the advancements in rs-fMRI and the deep learning approach, unique ways have been developed to introduce a diagnosis system for high-order neuro-dynamic functional networks using various levels, which motivated us to create such a classification model. Results of the study claimed optimal performance with the D2 model using three bands (slow4, slow5, and full-band) without any external feature selection compared to other models.

#### **3. Methods and Materials**

From the literature, it was deduced that low-order neurodynamics precisely manipulate the mono-band frequency span of resting-state functional magnetic imaging (rs-fMRI), leaving out the high-order neurodynamics. These were then hypothesized to be outperformed by DL techniques. Experimentally, we also propose an automated AD diagnosis system by developing a high-order neuro-dynamic functional network using various levels such as slow4, slow5, and full-band ranges (0.027 to 0.08 Hz), (0.01 to 0.027 Hz), and (0.01 to 0.08 Hz) of rs-fMRI time-series data.

#### *3.1. ADNI Dataset*

ADNI (https://adni.loni.usc.edu/ accessed on 1 September 2022) comprises multimode neuroimages of people who have Alzheimer's disease and is developed by the National Institute of Aging (NIA). The ADNI database consists of three classes of biological markers: AD, MCI, and NC. A total of 153 baseline subjects were selected. Table 1 shows the demographic details of the selected rs-fMRI subjects.

The MRI protocol for ADNI1 (2004–2009) focused on consistent longitudinal structural imaging with 1.5T scanners using T1- and dual-echo T2-weighted sequences. One-fourth of ADNI1 subjects were scanned using the same protocol on 3T scanners. ADNI-GO/ADNI2 (2010–2016) imaging was performed at 3T with T1-weighted imaging parameters similar to ADNI1. In place of the dual-echo T2-weighted image from ADNI1, 2D FLAIR and T2 weighted imaging were added at all sites. Both fully sampled and accelerated T1-weighted images were acquired in each imaging session.

**Table 1.** The demographic of the rs-fMRI subjects for our proposed model.

#### *3.2. Data Pre-Processing*

SPM 12 was used to pre-process the ADNI dataset and segment it into grey, white, and cerebrospinal fluid planes. The initial ten volumes were removed to permit dynamic equilibrium in each subject. All the slices were resampled with the slice-time correction to provide uniformity in time variation. Here, the middle slice was taken as a reference. It is followed by the realignment technique based on the reference slice. The individual averaged functional slices were co-registered using the landmark-based registration technique to their corresponding MRI. Later, the segmentation process was performed to extract the brain parts such as White Matter (WM), Gray Matter (GM), and cerebrospinal fluid (CSF). Every fMRI slice was resized/normalized to MNI (Montreal Neurological Institutes) space, and resampling was performed with a 3 <sup>×</sup> <sup>3</sup> <sup>×</sup> 3 mm<sup>3</sup> setting.

A Gaussian kernel was used for smoothing. Last, low frequencies are categorized based on their ranges—slow4 (0.027 to 0.08 Hz), slow5(0.01 to 0.027 Hz), and full-band (0.01 to 0.08 Hz). Zhang et al. [25] proposed the new model," hybrid high order fully connected networks", to describe the previously unenclosed intermediary relationship between down and up-order brain networks, getting the highest accuracy. Even the existing model was not able to address the dynamic brain changes. This work proposes a novel method using an automated AD diagnosis system by developing a high-order neuro-dynamic functional network using various frequency levels (slow4, slow5, and full-band) of rs-fMRI time-series data. Another common transformation is the imaging time series, which converts time series into images. One significant benefit of this transformation is the ability to retrieve data for any two time points given a time series. These imaging time series have been classified using deep neural networks [26], particularly convolutional neural networks.

Higher-order functional brain connections across several frequency bands are used in customized deep-learning models to distinguish AD and MCI from normal healthy levels. Thus, the combination of higher-order dynamic and frequency division-based brain networks opens a new window into diagnosing AD. We have used an "ensemble process" to increase the current model's performance by integrating many models into a single robust model. Figure 1 illustrates the complete workflow of the proposed model using deep neural networks.

The SPM12 software [27] and the toolboxes DPARSF (Data Processing Assistant for rsMRI) [28] and REST (Resting-state fMRI Data Analysis Toolkit) [29] were used to process the input scans. The initial ten volumes were removed to permit the dynamic equilibrium in each subject. Normalization for images has been performed, by which they were normalized from 0 to 1. We used the Inception V2 architecture [30] to identify abnormalities in the brain and detect them, leading to better results with less computational effort. The primary aim of this network was to select a particular layer at each level. This Inception V2 network uses a single filter size on the input brain MRI image (1 × 1) for which max pooling action is involved as a result of this inclusion.

**Figure 1.** Proposed model using deep networks. **Figure 1.** Proposed model using deep networks.

The SPM12 software [27] and the toolboxes DPARSF (Data Processing Assistant for rsMRI) [28] and REST (Resting-state fMRI Data Analysis Toolkit) [29] were used to process the input scans. The initial ten volumes were removed to permit the dynamic equilibrium in each subject. Normalization for images has been performed, by which they were nor-This neuro-dynamic functional network provided better accuracy, sensitivity, and specificity, apart from being speedy and efficient for detecting AD using rs-fMRI images. The ADNI image dataset's performance has been evaluated using various metrics, such as recall, specificity, and overall accuracy.

to increase the current model's performance by integrating many models into a single robust model. Figure 1 illustrates the complete workflow of the proposed model using

malized from 0 to 1. We used the Inception V2 architecture [30] to identify abnormalities in the brain and detect them, leading to better results with less computational effort. The 1. Disease Identification method: This is used to classify AD images collected by a medical expert during screening/monitoring programs.

primary aim of this network was to select a particular layer at each level. This Inception V2 network uses a single filter size on the input brain MRI image (1 × 1) for which max 2. Computer-Assisted Diagnosis: These methods are used to find the chances of disease based on rs-fMRI image changes.

3. Biomarkers: These are used for evaluating AD disease according to its severity.

pooling action is involved as a result of this inclusion. This neuro-dynamic functional network provided better accuracy, sensitivity, and specificity, apart from being speedy and efficient for detecting AD using rs-fMRI images. This paper aimed to instigate an ML and CNN method for classifying AD from normal controls.

#### The ADNI image dataset's performance has been evaluated using various metrics, such *3.3. Methods*

#### as recall, specificity, and overall accuracy. 3.3.1. Customized AlexNet

deep neural networks.

1. Disease Identification method: This is used to classify AD images collected by a medical expert during screening/monitoring programs. 2. Computer-Assisted Diagnosis: These methods are used to find the chances of disease based on rs-fMRI image changes. 3. Biomarkers: These are used for evaluating AD disease according to its severity. This paper aimed to instigate an ML and CNN method for classifying AD from normal controls. The final three layers are replaced to solve the issue and achieve maximum accuracy. The last three layers of AlexNet—FC, SoftMax, and classification layer—replace the pretrained network. These layers with the altered hyperparameters were eventually included by fine-tuning the previous layers and training the new layer of the AlexNet model using the ADNI dataset. The pre-trained model improved classification using the extensive ImageNet database using the feature extraction method. Minor tweaks are needed for the pre-trained parameters to adjust to the new MRI brain images. The modified hyperparameters define a small portion of the freshly transferred network.

Transfer learning is an essential statistical model for developing an efficient DL strategy. The critical regions of the brain can be recognized from MRI images by using newly updated parameters in a pre-trained network. These models have good convergence and are primarily used to extract the features and their classification. For this parameter learning, stochastic gradient descent with momentum optimizer is used.

As an extension to CNNs, the customized AlexNet architecture was developed to be competitive at the object detection task. Our proposed model achieves good performance using three bands without any external feature selection, reducing the task of exhaustive search using a set of heuristic approaches.

#### 3.3.2. Customized Inception V2

In this section, the Inception V2 CNN architecture is presented. For AD detection, TI-weighted MR images and non-invasive methods are used.

The Inception V2 architecture being shown may identify abnormalities in the brain and detect them, leading to better results with less computational effort. The main goal of this network is to select a particular layer at each level. This Inception V2 network utilizes a single filter size on the input brain MRI image (a1 × a1). A max-pooling action is included as a result of the inclusion. The inception V2's four pipes operate simultaneously. The architecture employs (1 × 1 × 1) filters in the first block to decrease the network by reducing the dimensions. The network begins with three convolutions dimension (a1 × a1 × a1, b3 × b3 × b3) matrix, which is comparable to the traditional network approaches (c5 × c5 × c5), (a1 × b3), and (c3 × 1). The filter size in the conventional network modal is (b3 × b3), which is divided into (a1 × b3) and (b3 × a1) convolutions. As an example, convolutions in the form of (b3 × b3) or (c5 × c5) are comparable to convolutions in the form of (a1 × b3) or (a1 × c5) and need less computation than (b3 × b3) dimension convolutions. In addition, the network comprises only two convolutions (a1 × a1 × a1); in the third part, it has only the pooling layer, and the fourth has only (a1 × a1 × a1) filters of convolution.

Similarly, all bands—aside from the max-pooling operation—begin with (a1 × a1 × a1) convolution filters. All conventional network layers are subjected to batch normalization to expedite training and reduce the risk of overfitting. After batch normalization is implemented, the convolution network improves and paves the way for data regularization in each network's hidden layers; finally, a leaky ReLU function, the activation function, is implemented. The slope of this function is marginally negative at (0.01). The function's slope is somewhat negative (0.01, or so on). The process is as follows in Equation (1):

$$f(\mathbf{x}) = \mathbf{1}(\mathbf{x} < \mathbf{0})(\mathbf{x}a) + \mathbf{1}(\mathbf{x} \ge \mathbf{0})(\mathbf{x}) \tag{1}$$

where *α* denoted as a negligible constant. The suggested Inception V2 has n properties, as shown in Figure 2, based on the input. In Figure 2, ∗ symbol indicates the multiplication operation. The primary advantage of the proposed model is the drastically reduced number of network parameters. The network's primary goal is to transfer specific data from the origin to the target feature space. The primary notion of this network has changed the feature space of the spatial data from source to destination. The CONV 3D (s.m) represents the three-dimensional convolution with size (S) and filters (m), whereas the max pool threedimensional (p.q) represents the three-dimensional max pool layer for down-sampling with the stride IQ and size of pool P. The convolution's filter size *nxn* has been divided into *ixn* and *nxi* convolutions. It is demonstrated that (a1 × b3) or (a1 × c5) convolutions, which perform (b3 × a1) or (c5 × a1) convolutions and are an output of the last layer, are comparable to (b3 × b3) or (c5 × c5) dimension convolutions. Finally, (b3 × b3), which is less expensive than other convolutions, is the concentration of two convolutions.

The customized model still has its limitations. Pre-trained models such as VGG/inception often produce valuable features. The big difference is the formation of the problem, especially the VGG/inception, which was designed for multiclass classification, which means learning a lot of irrelevant information. All the issues can be solved by fine-tuning a pre-trained VGG argument with a few layers augmented with a few layers for binary classification, thus changing the intra-network AD vs. MCI, MCI vs. NL, and NL vs. AD. The weight stored internally can also be too much, requiring additional regularization. During the design of the network, we incorporated an Adam optimizer to require fewer parameters for tuning and implement a faster computation time.

lutions.

**Figure 2.** Inception V2 network architecture. **Figure 2.** Inception V2 network architecture.

The customized model still has its limitations. Pre-trained models such as VGG/inception often produce valuable features. The big difference is the formation of the problem, especially the VGG/inception, which was designed for multiclass classification, which means learning a lot of irrelevant information. All the issues can be solved by finetuning a pre-trained VGG argument with a few layers augmented with a few layers for binary classification, thus changing the intra-network AD vs. MCI, MCI vs. NL, and NL vs. AD. The weight stored internally can also be too much, requiring additional regularization. During the design of the network, we incorporated an Adam optimizer to require In the traditional training method, the learning rate always remains the same. Recently, it has been suggested that the learning rate should be gradually changed, but this method has not been used in migration learning. Frequent changes in the learning rate not only accelerate the network convergence but also solve the problem that the loss value oscillates and is challenging to converge, with the learning weight also being gradually reduced. In top-level network training, better weight parameters are learned. A set of experiments are carried out to ensure the enhanced convergence rate of the model with improved recognition accuracy. The hyperparameter value is shown in Table 2.

(s.m) represents the three-dimensional convolution with size (S) and filters (m), whereas the max pool three-dimensional (p.q) represents the three-dimensional max pool layer for down-sampling with the stride IQ and size of pool P. The convolution's filter size has been divided into and convolutions. It is demonstrated that (a1 × b3) or (a1 × c5) convolutions, which perform (b3 × a1) or (c5 × a1) convolutions and are an output of the last layer, are comparable to (b3 × b3) or (c5 × c5) dimension convolutions. Finally, (b3 × b3), which is less expensive than other convolutions, is the concentration of two convo-

fewer parameters for tuning and implement a faster computation time. **Table 2.** Training Parameters for Customized AlexNet and Inception V2.

#### Batch size 4 3.3.3. Ensemble Deep Learning Model (D2)

**Training Parameter Value**

<sup>ε</sup> (epsilon) <sup>1</sup> <sup>×</sup> <sup>10</sup>−<sup>7</sup>

Epochs 50 L1 (learning rate) 1 × 10−5 Op (optimizer) Adam optimizer The customized models of Alexnet and Inception V2 described in the previous sections are separately tested on the dataset. The ensemble output is then created by adding the probabilities of each participant's output.

β\_1 0.9 β\_2 0.999 ε (epsilon) 1 × 10−7 The ensemble process merges various learning algorithms to gain their collective performance or to enhance the performance of current models by mixing different models to produce one trustworthy model. An ensemble framework works best when the participating systems are statistically varied because ensemble learning attempts to assemble complementing information from its numerous contributing models. Information fusion for improving classification performance is the primary justification for employing an ensemble learning model. To acquire a more reliable result, models trained using various data distributions related to the same set of classes are used while making predictions. The primary sources of error in learning models are noise, variation, and bias. Deep learning (DL) algorithms are accurate and stable due to the ensemble methods' capacity to reduce these error-causing elements. SVM and KNN are two different learning methods. SVM makes the quite restricted assumption that a hyperplane separates the data points. In contrast, KNN attempts to approximate the underlying distribution of the data in a non-parametric way.

#### **4. Experimentation Setup and Results Analysis 4. Experimentation Setup and Results Analysis**

non-parametric way.