ASSESSING BIPEDAL LOCOMOTION: TOWARDS REPLICABLE BENCHMARKS FOR ROBOTIC AND ROBOT-ASSISTED LOCOMOTION

EDITED BY : Diego Torricelli, Jan Veneman, Jose Gonzalez-Vargas, Katja Mombaur and C. David Remy PUBLISHED IN : Frontiers in Neurorobotics, Frontiers in Robotics and AI, Frontiers in Bioengineering and Biotechnology and Frontiers in Human Neuroscience

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ISSN 1664-8714 ISBN 978-2-88963-270-1 DOI 10.3389/978-2-88963-270-1

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## ASSESSING BIPEDAL LOCOMOTION: TOWARDS REPLICABLE BENCHMARKS FOR ROBOTIC AND ROBOT-ASSISTED LOCOMOTION

Topic Editors:

Diego Torricelli, Spanish National Research Council (CSIC), Spain Jan Veneman, Hocoma (Switzerland), Switzerland Jose Gonzalez-Vargas, Otto Bock (Germany), Germany Katja Mombaur, Heidelberg University, Germany C. David Remy, University of Michigan, United States

Citation: Torricelli, D., Veneman, J., Gonzalez-Vargas, J., Mombaur, K., Remy, C. D., eds. (2019). Assessing Bipedal Locomotion: Towards Replicable Benchmarks for Robotic and Robot-Assisted Locomotion. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88963-270-1

# Table of Contents

*05 Editorial: Assessing Bipedal Locomotion: Towards Replicable Benchmarks for Robotic and Robot-Assisted Locomotion* Diego Torricelli, Jan Veneman, Jose Gonzalez-Vargas, Katja Mombaur and

C. David Remy

*08 Golden Gait: An Optimization Theory Perspective on Human and Humanoid Walking*

Marco Iosa, Giovanni Morone and Stefano Paolucci

*15 Comparing Gait With Multiple Physical Asymmetries Using Consolidated Metrics*

Tyagi Ramakrishnan, Christina-Anne Lahiff and Kyle B. Reed

*27 Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals From Wearable Sensors During Unassisted Locomotion in Able-Bodied Individuals*

Blair Hu, Elliott Rouse and Levi Hargrove

Diego Torricelli, Camilo Cortés, Nerea Lete, Álvaro Bertelsen, Jose E. Gonzalez-Vargas, Antonio J. del-Ama, Iris Dimbwadyo, Juan C. Moreno, Julian Florez and Jose L. Pons

Mark Vlutters, Edwin H. F. Van Asseldonk and Herman van der Kooij

*85 Fusion of Bilateral Lower-Limb Neuromechanical Signals Improves Prediction of Locomotor Activities*

Blair Hu, Elliott Rouse and Levi Hargrove

Daniel F. N. Gordon, Graham Henderson and Sethu Vijayakumar

*130 Leg Force Control Through Biarticular Muscles for Human Walking Assistance*

Maziar A. Sharbafi, Hamid Barazesh, Majid Iranikhah and Andre Seyfarth

Khai-Long Ho Hoang, Sebastian I. Wolf and Katja Mombaur

# Editorial: Assessing Bipedal Locomotion: Towards Replicable Benchmarks for Robotic and Robot-Assisted Locomotion

Diego Torricelli 1 \*, Jan Veneman2 , Jose Gonzalez-Vargas 3 , Katja Mombaur 4 and C. David Remy 5

1 Spanish National Research Council (CSIC), Madrid, Spain, 2 Hocoma AG, Volketswil, Switzerland, 3 Ottobock SE & KGaA, Duderstadt, Germany, 4 Optimization, Robotics & BIomechanics (ORB), Institute of Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany, 5 Institute of Nonlinear Mechanics, University of Stuttgart, Stuttgart, Germany

Keywords: benchmarking, wearable robots, humanoids, walking, balance, prosthetics and exoskeletons

**Editorial on the Research Topic**

**Assessing Bipedal Locomotion: Towards Replicable Benchmarks for Robotic and Robot-Assisted Locomotion**

## INTRODUCTION

#### Edited by:

Florian Röhrbein, Technical University of Munich, Germany

Reviewed by: Qining Wang, Peking University, China

\*Correspondence: Diego Torricelli<EMAIL_ADDRESS>
Received: 11 April 2019 Accepted: 03 October 2019 Published: 25 October 2019

#### Citation:

Torricelli D, Veneman J, Gonzalez-Vargas J, Mombaur K and Remy CD (2019) Editorial: Assessing Bipedal Locomotion: Towards Replicable Benchmarks for Robotic and Robot-Assisted Locomotion. Front. Neurorobot. 13:86. doi: 10.3389/fnbot.2019.00086 Human-centered bipedal robots, such as exoskeletons, powered prostheses and humanoids are demonstrating increasing levels of functionality, reliability, and safety, and are now breaching the barrier of surviving in a rapidly evolving market. In this crucial process, the lack of accepted standards to evaluate the different facets of robotic performance is hampering the efficient introduction of these new technologies into the different application domains, each of them characterized by the needs, requirements, and regulations of diverse users. A systematic benchmarking methodology to assess robotic systems on a quantitative and reproducible basis is eagerly awaited, as demonstrated by the numerous workshops and discussions on this topic in the most relevant international forums of the last 5–10 years. Benchmarks allow comparing systems to each other and against accepted references, and are therefore useful not only to end-users in search for the most suitable solution, but also to developers aiming to identify and solve the critical shortcomings of their machines. Solid benchmarks usually arise from research results, and are eventually converted into international standards after an iterative process that can last years. It can be argued that the more effort is done by the research community in identifying effective benchmarking methods, the easier it will be to arrive at a complete and relevant set of standards that will improve and accelerate the introduction of new robots in people's lives.

In the field of bipedal walking and posture, a huge body of literature focused on understanding or replicating human performance. Yet, just a few works specifically focused on proposing reproducible metrics that could be potentially converted into benchmarks for human-centered robotic devices. This Research Topic is a first step in this direction. We gathered a total of 16 original research articles from the fields of gait analysis, motor control, simulation, robotics and rehabilitation, covering together most of the aspects of bipedal skills, with a clear focus on identifying measurable metrics of performance. We believe and hope that the works presented in this collection can demonstrate how the process of identifying useful benchmarks of human and human-like robotic motion is possible, and encourage the scientific community to join the efforts in this ambitious goal.

## ASSESSING, UNDERSTANDING, AND REPLICATING BIPEDAL LOCOMOTION

In science, quantitative measures are necessary to understand the basic principles behind behavior and to validate or create scientific hypotheses. In robotics, performance measures are mainly used to achieve—and demonstrate—a certain expected level of performance. With the advent of human-inspired robotics, which aims to integrate biological principles into human-like or human-compatible machines, these two facets become strongly intertwined. Replicating human motion into real-life machines requires a deep understanding of the neuromechanical mechanisms at the basis of human behavior and, at the same time, the achievement of levels of performance comparable with those of the human counterpart. Assessing the performance of bipedal systems, being them humans, robots, or the combination of both (as happens in wearable robotics), is therefore a necessary component to bridging the two sides of the same coin: understanding and replicating humanlike locomotion. The articles collected in this Research Topic provide a remarkable example of how these three aspects are strongly enlaced, and how they can be translated into domainspecific metrics.

Three articles of this collection specifically focused on understanding and quantifying human balance, which is a crucial aspect for both walking and standing. Vlutters et al. aim to understand how humans deal with balance perturbations during walking, by experimentally testing reactions after pushes with different amplitudes, direction, and timing. Using simple metrics based on foot placement location, placement timing, and COM velocity, they demonstrate that human response is highly dependent on the timing of the perturbation, and potentially usable as a benchmark of human-like balance reactions. Mergner and Lippi present a comprehensive framework to test the posture control of human-like bipedal systems. The framework, based on a deep knowledge of the sensorimotor mechanisms of human balance, describes the experimental procedures to replicate the most relevant physical disturbances, and the metrics to quantify both performance and human likeness. Liu et al. investigate the causal relationship between spatiotemporal asymmetries and impaired reactive control of balance, by using two main metrics, i.e., whole-body angular momentum and Floquet analysis. These two metrics may be useful in grasping stability measure in impaired and robot-assisted gait.

Five articles focused on metrics and criteria aimed to evaluate or predict the effects of walking with prosthetic devices. Hu et al., based on the fact that bipedal walking requires interlimb coordination, demonstrate that neuromechanical information from both limbs is necessary to improve the prediction of transitions between level walking and stairs or slopes. This work, which has potential benefits for the design of more intuitive and transparent lower limb prostheses, is linked to a datasets of neuromechanical signals from wearable sensors, recorded bilaterally, which has been made available to the community for benchmarking purposes (Hu et al.). Ramasamy et al. propose a novel and subject-specific modeling tool to predict the interaction between a residual limb and a prosthetic device, providing relevant evidence to improve the prediction the potential sites of deep tissue injury generated by the socketstump interaction forces. Ramakrishnan et al. present a new metric, called combined gait asymmetry metric (CGAM), which provides a unified index of gait asymmetry that combines 11 spatio-temporal, kinematic, and kinetic parameters. This metric has promising potential in the prosthetic field to assess and categorize asymmetries due to different natures, such as leg length or added mass at the leg. Ho Hoang et al. investigated a number of benchmarking criteria to assess the subject-specific strategy of maintaining stability during unimpaired and impaired (i.e., transfemoral amputees) walking. By using optimal control to dynamically reconstruct recorded motions, they showed that human-like stability can be explained by two main indicators, i.e., foot placement and the Residual Orbital Energy.

Five articles proposed metrics and models to estimate and predict the effects of the interaction between humans and lower limb exoskeletons. Mummolo et al. propose an algorithmic framework to evaluate the balance stability of exoskeletons in crutch-less configurations during flat ground walking. They propose new metrics based on center of mass and joint-space dynamics and applied them to the lowerlimb exoskeleton Mina-2, demonstrating how this method has interesting potential for the assessment of the future generation of self-balancing exoskeletons. Torricelli et al. proposes a subject-specific musculoskeletal model able to infer the relative motion between the exoskeleton and the human limbs from the kinematic data recorded from the exoskeleton. This approach may contribute to improve predictive control strategies and/or mechanical designs for better humanrobot interaction. Gordon et al. applied a set of metrics based on dynamic stability and metabolic cost estimations to exoskeleton-assisted walking under varying conditions (speed, floor inclination, exoskeleton assistance), selecting some of them as potential control variables for adaptive exoskeletons. Gandolla et al. propose an automatic and patient-specific calibration procedure able to detect the best setting control parameters of the lower limb exoskeleton Ekso. The method is based on the maximization of the Gait Metric index, which quantifies the similarity of muscle activations to a reference normative set. Sharbafi et al. propose a control algorithm for exoskeletons based on the virtual pivot point (VPP) concept and simulate its effects on a detailed neuromuscular model of human walking. They showed that, with one cable-driven biarticular actuator, the exoskeleton can reduce subject muscle activation and metabolic cost, while ensuring balance stability during walking.

A group of three articles focused on experimental and theoretical methods for the evaluation of human-like performance of bipedal machines, in either real or simulated environments. Stasse et al. investigated the effect of temperatures variation on the walking performance of the HRP-2 robot, computing a set performance indicators previously defined by Torricelli et al. (2015) across different tasks, such as standing on tilting and horizontally moving surfaces, and walking during pushes, uneven or inclined surfaces, with the aim of testing the capability of a humanoid robot in real-life situations. Okajima et al. developed a control algorithm able to generate different human-like bipedal behaviors by adjusting a few signals in a symbolized control space, with the help of a tacit learning approach. This work has potential benefit not only in simplifying the control of a humanoid robot, but also in understanding the mechanisms at the basis of human-like walking and standing. Iosa et al. propose the golden ratio as the trade-off between advancement and equilibrium managed during walking, providing additional theoretical evidence in favor of the golden ratio as a descriptor of harmonic gait. This approach may be extended to assess the harmony in humanoid robotic walkers or neurorobots for rehabilitation.

## CONCLUSIONS AND FUTURE PERSPECTIVES

The large number of articles collected in this Research Topic are in itself proof of the substantial interest that different research communities have in the definition of reliable metrics of bipedal performance.

In the prosthetic field, we observed a clear need of metrics able to predict the interaction between the prosthetic device and human, as well as to measure the functional effects of walking in a real environment, in presence of transitions between different terrains. In the exoskeleton field, there is an increasing interest in identifying strategies for more adjustable/adaptable machines, under the perspective of control, actuation, or ergonomics. In the humanoids field, a promising trend is to test the performance in outdoor-like scenarios under a wide variety of perturbations, e.g., moving grounds or pushes.

The high heterogeneity of the approaches even further highlights the need for a unified framework in which these views are brought together and can be easily shared across the research community. Promising efforts in this direction are currently promoted by two European initiatives: the EUROBENCH project "European Robotic Framework for Bipedal Locomotion Benchmarking" (Torricelli and Pons, 2019, www.eurobench2020. eu) and the COST action CA16116 "Wearable Robots for Augmentation, Assistance or Substitution of Human Motor Functions" (https://www.cost.eu/actions/CA16116). Both initiatives aim to establish common methodologies and promote the discussion on standardized assessment of robotic systems, focused but not limited to bipedal locomotion.

## REFERENCES

This research field needs to address some important bottlenecks. First, the great majority of methods proposed in the literature are applied to individual devices or specific experimental settings. While many works claim a potential applicability of their metrics across different laboratory conditions or devices, very few of them actually have demonstrated such wider applicability. Nevertheless, this issue is critical for benchmarking, of which the principal goal is to compare different systems under reproducible conditions. This is particularly relevant considering the important trade-off between simplicity and reproducibility, validity of a benchmark and relevance of the outcome, considering the many subtle differences in applications, e.g., differences across industrial environments, or differences in pathologies across subjects.

We also observed an intrinsic difficulty in comparing bipedal robot performance with human performance. The concept of "human likeness," intuitively easy to grasp, becomes hard to define under a rigorous and systematic way, especially in robotics systems that have intrinsic differences (e.g., kinematic configuration) with humans, or when the human is not the golden standard for performance (similarly to the case of chess players). New challenges and questions (e.g., is it useful to compare robots with humans?) may be object of future research.

We believe that reaching an international consensus on these topics will be extremely beneficial to boost the process of finding reliable methods to test and compare different systems and identifying robust metrics to measure the Technology Readiness Level (TRL) of new robotic solutions.

## AUTHOR CONTRIBUTIONS

DT drafted the initial version of the manuscript. All authors have made substantial and iterative contribution to the work, and approved its final version for publication.

## ACKNOWLEDGMENTS

We thank all authors contributing with their work to this Research Topic. We also acknowledge all the European Projects that have supported past and current research in this topic: the FP7 Projects H2R (grant 600698), BALANCE (grant 601003), KoroiBot (grant 611909), WALK- MAN (grant 611832), BioMot (grant 611695), and the currently ongoing H2020 Project EUROBENCH (grant 779963), and COST Action CA16116.

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Torricelli, Veneman, Gonzalez-Vargas, Mombaur and Remy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Golden Gait: An Optimization Theory Perspective on Human and Humanoid Walking

### Marco Iosa\*, Giovanni Morone and Stefano Paolucci

Clinical Laboratory of Experimental Neurorehabilitation, IRCCS Fondazione Santa Lucia, Rome, Italy

Human walking is a complex task which includes hundreds of muscles, bones and joints working together to deliver harmonic movements with the need of finding equilibrium between moving forward and maintaining stability. Many different computational approaches have been used to explain human walking mechanisms, from pendular model to fractal approaches. A new perspective can be gained from using the principles developed in the field of Optimization theory and in particularly the branch of Game Theory. In particular we provide a new insight into human walking showing as the trade-off between advancement and equilibrium managed during walking has the same solution of the Ultimatum game, one of the most famous paradigms of game theory, and this solution is the golden ratio. The golden ratio is an irrational number that was found in many biological and natural systems self-organized in a harmonic, asymmetric, and fractal structure. Recently, the golden ratio has also been found as the equilibrium point between two players involved into the Ultimatum Game. It has been suggested that this result can be due to the fact that the golden ratio is perceived as the fairest asymmetric solution by the two players. The golden ratio is also the most common proportion between stance and swing phase of human walking. This approach may explain the importance of harmony in human walking, and provide new perspectives for developing quantitative assessment of human walking, efficient humanoid robotic walkers, and effective neurorobots for rehabilitation.

#### Edited by:

Diego Torricelli, Consejo Superior de Investigaciones Científicas (CSIC), Spain

#### Reviewed by:

Erik Prinsen, Roessingh Research and Development, Netherlands Stefan Schuster, Friedrich Schiller, Universität Jena, Germany

#### \*Correspondence:

Marco Iosa<EMAIL_ADDRESS>
Received: 25 September 2017 Accepted: 08 December 2017 Published: 19 December 2017

#### Citation:

Iosa M, Morone G and Paolucci S (2017) Golden Gait: An Optimization Theory Perspective on Human and Humanoid Walking. Front. Neurorobot. 11:69. doi: 10.3389/fnbot.2017.00069 Keywords: walking, neuroscience, anthropometry, rehabilitation, divine proportion, golden section, fractal, game theory

## INTRODUCTION

Optimization theory is a branch of mathematics aiming at identifying the best choice, from some set of available alternatives, that optimizes (maximizes or minimizes) a specific target function (Asghar Bhatti, 2000). A well-known application of optimization theory to human locomotion refers to the fact that the comfortable walking speed is, for healthy individuals, that minimizing the energy consumption (Ralston, 1958; Miller et al., 2012; Oh et al., 2012; Long and Srinivasan, 2013; Seethapathi and Srinivasan, 2015).

Game Theory is a branch of Optimization Theory in which there is not just one function to optimize, but there is the need to identify the best compromise among some entities involved into the problem (Kolokoltsov and Malafeyev, 2010). Game Theory has become a large and powerful theoretical framework providing mathematical models for predicting the choices of rational entities (usually called players) in conflict or in cooperation tasks (Rapoport, 1974; Sanfey, 2007). Mainly used in psychology, economy, political science, logic, computer science, Game Theory has also been enlarged to biology (Maynard Smith, 1982). Following this approach, game theoretical methods have been used in biochemistry and biophysics (Schuster et al., 2008), with some studies considering cells (Gatenby and Vincent, 2003) and even molecules (Bohl et al., 2004) as ''players'' working together or being in competition for the same objective.

The idea of applying game theory to human walking proposed in this article originates by the observation that current advances in these so different research fields reported the same solution for two apparently different problems. In fact, the same equilibrium point was found in human walking and Ultimatum game: this point coincides with the so-called golden ratio.

The golden ratio (φ) is the solution of the problem already reported by Euclid in III century B.C. to cut a given straight line so that the proportion between the shorter part to the longer one is the same as the longer part to the whole. It is an irrational number already found in many physical, biological fractal structures that are self-organized so that the larger-scale structure resembles the subunit structure (King et al., 2004; Yamagishi and Shimabukuro, 2008). In fact, it was found in structures of animal bodies (Livio, 2003) and plants' leaves (Okabe, 2011), in the solar systems (Lombardi and Lombardi, 1984), replicated in architecture (Hemenway, 2005) and in certain musical rhythms (Garland, 1995), as well as in financial market patterns (Agaian and Gill, 2017). In humans, harmonic proportions have been found in the physiological activity of the heart (Yetkin et al., 2013) and in anthropometry (Davis and Altevogt, 1979), as depicted in figurative arts (Hemenway, 2005). In general, the golden ratio has been found as the best choice for many biological processes (Bartl et al., 2010; Yetkin et al., 2013; Schuster et al., 2017).

## GAME THEORY: THE ULTIMATUM GAME EQUILIBRIUM

The Ultimatum game is a model game developed for analyzing fairness, and it is one of the most famous paradigms of Game Theory. In this game, two players must divide a sum of money: the proposer has to specify this division and the responder has the option of accepting or rejecting the offer (Sanfey, 2007). If the offer is accepted, the sum is divided as proposed, otherwise neither player receives anything. A rational intelligence should accept any offer and, knowing this, the proposer could offer the smallest nonzero amount (this choice is called Nash equilibrium). However, most of responders reject small proposals because judged unfair. Another point of equilibrium predicted by game theory is an offer split in 50% and 50% proportion (equipartition).

But most of the studies reported that the two solutions reported above are not the most common, and the average offer ranges around 62% for player 1 and 38% for player 2 (Oosterbeek et al., 2004; Henrich and Silk, 2013).

Some recent researchers (Schuster, 2017; Suleiman, 2017) noted that this division, chosen by subjects as the fairest one, is very close to the value of the golden ratio, and it was due to the fact that responders may tend to accept an amount that is in a proportion with that taken by proposer when this proportion is

FIGURE 1 | Schematic representation of the Ultimatum Game of Game Theory. Above the amount taken by player 1 (red line) and that taken by player 2 (blue line). The dash line is the Nash equilibrium (chosen by completely rational intelligence that accepts each offer higher than zero), the solid line the equipartition (fifty-fifty), the dot line the golden ratio solution (golden equilibrium, that observed in experimental data). Below, the offer and gain are expressed as proportions: red line is the offer ratio (expressed in percentage of whole amount) and the blue line is the gain of player 2 (with respect to that taken by player 1). The equilibrium is given by golden ratio solution (partition: about 38.2% for player 2 and 61.8% for player 1): player 2 tends to accept an offer of the minor fraction of φ (38.2%) because they accept a proportion of that taken by player 1 if this latter sum is in the same proportion with the whole amount.

the same between the latter part remaining at the proposer with respect to the whole amount.

**Figure 1** theoretically depicts the Ultimatum Game. This ''equality of fractions'' is conceivably perceived by both players as the fairest asymmetric division (Schuster, 2017).

The Golden Ratio plays a role also in other games analyzed in game theory (Camerer et al., 2004; Berg et al., 2015) and in the so-called justice evaluation function (Jasso, 2007). The Ultimatum Game is even used in economics (Güth and van Damme, 1998), and as its solution, the golden ratio has been reported as an example of ''Economic Harmony'' (Suleiman, 2017).

## CURRENT ADVANCES IN HUMAN WALKING: THE GOLDEN GAIT

Many different computational approaches have been used to explain human walking mechanisms, from inverted pendulum

model (Ivanenko et al., 2007) to fractal approaches (Hausdorff et al., 1995).

The inverted pendulum model refers to the pendular trajectory of center of mass and to the relevant transfer from potential to kinetic energy and its reverse (Ivanenko et al., 2007; McGrath et al., 2015). This trajectory can be seen in the above plot of **Figure 2** showing the stick diagram, a typical figure formed linking anatomical points and obtained using stereophotogrammetric systems (Ivanenko et al., 2007). At comfortable speed (about 1.4 m/s), locomotor system saves energy by exchanging forward kinetic energy and gravitational potential energy of the center of mass during the invertedpendulum oscillation of stance (Cavagna and Margaria, 1966), and by ballistically oscillating the limb as a compound-pendulum during swing (Mochon and McMahon, 1980). Thus muscle activity is only required to oppose gravity, maintain postural configurations in the face of interaction torques, and reintegrate energy losses during each cycle (Lacquaniti et al., 2012). Inverted pendulum is a simplified model in which a rigid rod represents the leg during stance phase, other more sophisticated models take into account also the stiffness and the elastic properties of the lower limbs (Lipfert et al., 2012).

The fractal approach refers to the idea that gait has a fractal structure, id est the larger-scale structure resembles the subunit structure (Hausdorff et al., 1995). The simplest examples of fractals are the structures based on the golden ratio. Recently, the Golden ratio has been found in gait cycle (GC) as the proportion between the stance duration (the longer part of a stride) and the swing duration (the shorter part). When the stance and swing are in the golden ratio, gait phases revealed a fractal structure based on the property of autosimilarity, with the same proportionality emerging between units and consecutive subunits of gait, resembling in each part the same whole structure (Iosa et al., 2016d) as follows:

$$\begin{aligned} \phi &= \frac{1+\sqrt{5}}{2} = \frac{\text{swing}}{\text{double support}} = \frac{\text{state}}{\text{swing}} \\ &= \frac{\text{stride}}{\text{state}} = \frac{\text{stride}+\text{state}}{\text{stride}} = \frac{2\text{striides}+\text{state}}{\text{stride}+\text{state}} = \dots \end{aligned}$$

With the expression ''golden gait'' we refer to a gait in which the above equations are respected. Recent researches showed as golden gait can be a bridge between pendular model and fractal approach of walking (Iosa et al., 2016b). This relationship is based on the strict intertwine between structure and functioning, been the anthropometric proportion between the stature and the distance of center of mass from the ground close to the golden ratio (Davis and Altevogt, 1979), as also represented in the ancient Greeks' sculptures (Di Dio et al., 2007).

However, the golden gait is not only related to the biomechanics of human body and its anthropometric proportions. In fact, the pendulum mechanism of walking should be considered as a forced oscillator for the need of rhythmic intervention of muscle activity and hence of nervous system (West and Scafetta, 2003). It has been hypothesized that a neural network formed by cerebellum, globus pallidum and central pattern generators of spinal cord is the generator of the golden ratio harmonic rhythm of walking (Iosa et al., 2016d). This hypothesis can be supported by clinical data showing as the golden ratio based rhythm is altered in patients with cerebellar ataxia (Serrao et al., 2017) and Parkinson's Disease (Iosa et al., 2016d), but not directly in presence of a damage of cortical areas due to stroke (Iosa et al., 2016a).

## APPLYING GAME THEORY TO HUMAN WALKING

During walking there is only one subject, the walking person, but he/she has to manage between the need of advancing and the need of maintaining stability without falling.

The relationships between the gait phases, expressed in percentage of the whole GC (100%), can be written as follows:

$$\begin{array}{rcl} \text{St} &=& 100 - \text{Sw} \\ \text{DS} &=& \text{St} - \text{Sw}' \end{array}$$

where St = stance phase; Sw = swing phase; Sw0 = controlateral swing phase; DS = double support phase. For a symmetric walking Sw = Sw0 and hence:

$$\text{DS} = \text{St} - \text{Sw} = 100 - 2 \text{Sw}$$

The advancement depends on the speed of walking, the efficiency of walking optimized around the speed self-selected by subjects as the comfortable one (Cotes and Meade, 1960). Walking speed is linearly proportional to swing duration (Hebenstreit et al., 2015). So the advancement depends on the ratio swing/stance (Sw/St), with the limit of Sw/St < 1, otherwise (Sw > St) walking becomes running, with the absence of any double support phase (Mann and Hagy, 1980).

Gait stability has been warranted by the fact that stance phase is longer than swing phase, that generates the presence of a double support phase (DS), with both feet on the ground (Perry, 1992). In fact, the maintenance of equilibrium is due to a dynamic stability in which the fall of center of mass has been controlled by the swinging leg arriving to touch the ground before subject falls (Mrozowski et al., 2007). This stability can be increased by prolonged double support phase (Perry, 1992). Hence, upright stability is strictly related to the duration of the double support (DS) with respect to swing duration (DS/Sw).

Given the above equations, it is possible to report the two ratios DS/Sw and Sw/St (related to stability and advancement, respectively) as function of the only swing phase (Sw) as follows:

$$\begin{array}{rcl} \frac{\text{DS}}{\text{Sw}} & = & \frac{100 - 2 \cdot \text{Sw}}{\text{Sw}} \\ \frac{\text{Sw}}{\text{St}} & = & \frac{\text{Sw}}{100 - \text{Sw}} \end{array}$$

These ratios can be seen as analogous to the offer of player 1 and the gain of player 2, respectively. Below plot of **Figure 2** shows the curves described by these two functions. This approach (as well as this plot) clearly replicates that reported for gain and offer ratios of the Ultimatum Game curves (**Figure 1**).

In the range of swing phase values in which DS/Sw > Sw/St, the stability is favored over advancement. On the contrary, if DS/Sw < Sw/St, the advancement is favored over stability.

In this subplot of **Figure 2** we also superimposed to the curves the experimental data extracted by a previous study on walking in healthy adults (Iosa et al., 2016a) and recorded using an optoelectronic system during comfortable walking (for details see Iosa et al., 2016a). Data recorded at slow and fast walking were not reported in that study, but collected in the same experimental session and reported here as original data that agree with those of literature (Perry, 1992).

Similarly to the responses observed for the Ultimatum Game, experimental data converged to this Golden equilibrium between advancement and stability.

Schuster (2017) suggested also an alternative approach for explaining the golden solution as the convergent equilibrium of the Ultimatum Game. This alternative way of explanation in terms of bargaining corresponds with the convergence of the following continued fraction that is another property of the golden ratio:

$$\varphi = 1 + \frac{1}{1 + \frac{1}{1 + \frac{1}{1 + \dotsb}}}$$

The results of this progressive division are: 3/2, 5/3, 8/5. . . All these numbers (1, 2, 3, 5, 8. . .) are consecutive Fibonacci numbers and the limit of the ratio between two consecutive Fibonacci numbers is the golden ratio.

Also this approach can be applied to human walking. In fact, the two limits are fixed by static posture (maximum stability, no advancement) and the limit of transition between walking and running (Sw = St = 50%). The following schema could be hence derived applying Fibonacci's numbers (1, 2, 3, 5, 8) to walking, using the ratio between the gait cycle (GC) and the stance (St):

$$\begin{aligned} \frac{\text{GC}}{\text{St}} &= 1 + \frac{1}{1 + \frac{1}{1 + \frac{1}{1 + \dotsb}}} \\ \text{Posture: } \frac{\text{GC}}{\text{St}} &= 1 \rightarrow \text{(state = 100\%)} \\ \text{Transition to Ranning: } \frac{\text{GC}}{\text{St}} &= 1 + 1 = 2 \\ &\rightarrow \text{(state = 50\%)} \\ \text{Slow walking: } \frac{\text{GC}}{\text{St}} &= 1 + \frac{1}{2} = \frac{3}{2} \rightarrow \text{(state = 66.6\%)} \\ \text{Fast walking: } \frac{\text{GC}}{\text{St}} &= 1 + \frac{1}{1 + \frac{1}{2}} = \frac{5}{3} \rightarrow \text{(state = 60\%)} \\ \text{Comforstable walking: } \frac{\text{GC}}{\text{St}} &= 1 + \frac{1}{1 + \frac{1}{1 + \frac{1}{2}}} = \frac{8}{5} \cong \rho \\ &\rightarrow \text{(state = 62.5\%)} \end{aligned}$$

The foot-off occurring at the minima located at 5/8 of the GC approximates the golden ratio with a difference of only 0.7% of stride duration.

The most important similarity between Ultimatum Game and the stance/swing trade-off in GC is that both need an asymmetric equilibrium point. In fact, in the Ultimatum Game, subjects usually assume that the proposer has some priority because he has the whole amount and he is allowed to choose the offer (Ichinose, 2012). Similarly, walking cycle is characterized by the presence of a double support phase, existing only if St > Sw.

## FUTURE DIRECTIONS: FROM HOMINIDS TO HUMANOID WALKING, PASSING THROUGH ROBOT-ASSISTED WALKING

The experience of million of years of evolution between extreme solutions (posture and running) in search for optimal bipedal walking, seems to have achieved the golden compromise between stability and advancement, muscle work and efficiency (Massaad et al., 2007). This is confirmed by studies on patients, showing as the golden gait minimizes the energy cost optimizing the transfer between potential and kinetic energy (Serrao et al., 2017), and on virtual model of walking, tested in different initial conditions, always converging to the golden ratio as an equilibrium point (Dzelaidini et al., 2014). Surprisingly, even chimpanzee during bipedal walking showed a proportion between stance and stride that is about 0.65 (Demes et al., 2015), so that the inverse proportion is close to the golden ratio. It has been even hypothesized that humans evolved from hominids in the actual antrophometric dimensions in golden proportion for favoring this harmonic golden gait (Iosa et al., 2016b).

It is well known that patients with neurological impairments have an alteration of the percentage duration of stance and swing (Perry, 1992), but only recently this alteration was put in relationship to a deviation from the golden ratio (Iosa et al., 2016a,d; Serrao et al., 2017).

For years, it has been suggested that rehabilitation should drive walking patterns of individuals with a gait impairment at resembling the patterns of healthy individuals as closely as possible. However more recent results revealed a more complex scenario. Hak et al. (2014) showed that step length asymmetry in individuals with a transtibial amputation are functional in terms of stability: a training aiming at recovering gait symmetry may affect dynamic balance during gait in these subjects, as theoretically suggested earlier by Merker et al. (2011). Our perspective study can contribute in clarifying that the optimization should take into account more target functions at the same time, as in Game Theory, not just one. Future research should focus on a more in-depth understanding of how gait impairments influence human locomotion and which target functions should be trained for a better recovery of gait autonomy finding a favorable trade-off that could even be different from physiological one.

A clear example is that of humanoid robots, in which the choice of bioengineers is guided by the cost of these robots. For most of humanoid robots, walking is quite different from that of humans because the distance between the center of mass and ground is usually maintained constant (Massaad et al., 2007). These humanoid robots use sophisticated motor control to walk smoothly while demonstrating appalling inefficiency with excessive energy cost: in this case the management between stability and advancement has been achieved favoring the former one for reducing the risk of fall and hence of damages. In humans, a walk with a flat trajectory of center of mass need muscles working in unfavorable conditions, wasting energy (Massaad et al., 2007). From this point of view, passive-dynamic mechanical walkers, moving up and down their center of mass, rival with humans in terms of efficiency (Collins et al., 2005). Recently, a bipedal robot has been developed using a golden ratio algorithm for reproducing golden gait obtaining a harmonious walking patterns similar to those of humans (Tez and Kus¸çu, 2017).

Robots for assisting human walking during rehabilitation have already been made for replicating a ratio between stance and swing close to the golden ratio: 60% and 40% (Hesse et al., 2000) or 62% and 38% (Volpini et al., 2017). However, this proportion was chosen just according to the principle of driving patients' gait patterns resembling the physiological patterns. It is worthy to investigate whether the golden ratio can be applied in robotic-aided therapy not just because it is a physiological pattern, but because it is the best harmonious trade-off between stability and advancement. The golden gait, in fact, seems to be a key of efficiency for locomotor control and stimulating the sensori-motor system at the frequency of the golden ratio may facilitate the restoring of the harmonic activity of neural circuits at the basis of walking.

Neurorobots already have the potential for accurately assessing motor functioning, therapy progresses and for providing patients with stimulation and real-time feedback on movement performance (Iosa et al., 2016c). This approach should take into account the golden ratio, using this proportion for stimulating and quantitatively assessing gait harmony (Torricelli et al., 2014; Saner et al., 2017), as suggested for assessing electrocardiographic patterns with respect to the deviation from the golden ratio of the cardiac cycle ratios (Ciucurel et al., 2018).

## CONCLUSION

Despite the golden ratio has often been reported as a ''magic number'', it is just the solution of a simple problem of a geometrical problem already reported by Euclid in III century B.C. and then also found as solution adopted by many biological and physical systems, including fractal structures and human physiology (Iosa, 2016). It has also been judged as the fairest proportion for geometrical figures (Green, 1995) and anthropomorphic sculptures (Di Dio et al., 2007) in psychological studies.

## REFERENCES

In the Game Theory, the golden ratio was found as the best equilibrium for the Ultimatum Game because judged as the fairest solution by the players. Following a similar approach, we showed that the golden ratio is also the best solution for managing advancement and stability during human walking optimizing bipedal efficiency.

As Suleiman (2014) noted and Schuster (2017) reported, the word ''fair'' has a double meaning, that of equitable and beautiful. For human walking, golden gait is also the most efficient. Once again, we can learn a lesson from Ancient Greek for whom a beautiful harmonic structure is strictly intertwined with an efficient virtuous functioning.

## ETHICS STATEMENT

This perspective article include some data already published (and here only re-analyzed) in Iosa et al. (2016b).

## AUTHOR CONTRIBUTIONS

MI developed the idea and the application of game theory to human walking. GM critically reviewed the original manuscript. SP coordinated and supervisioned the writing process. All the authors contributed to the discussion and the development of article structure.

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Iosa, Morone and Paolucci. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Comparing Gait with Multiple Physical Asymmetries Using Consolidated Metrics

### Tyagi Ramakrishnan, Christina-Anne Lahiff and Kyle B. Reed\*

Rehabilitation Engineering and Electromechanical Design Laboratory, Department of Mechanical Engineering, University of South Florida, Tampa, FL, United States

Physical changes such as leg length discrepancy, the addition of a mass at the distal end of the leg, the use of a prosthetic, and stroke frequently result in an asymmetric gait. This paper presents a metric that can potentially serve as a benchmark to categorize and differentiate between multiple asymmetric bipedal gaits. The combined gait asymmetry metric (CGAM) is based on modified Mahalanobis distances, and it utilizes the asymmetries of gait parameters obtained from motion capture and force data recorded during human walking. The gait parameters that were used in this analysis represent spatio-temporal, kinematic, and kinetic parameters. This form of a consolidated metric will help researchers identify overall gait asymmetry by showing them if the overall gait symmetry is improving and avoid the case where one parameter's symmetry is improving while another is getting worse. The CGAM metric successfully served as a measure for overall symmetry with eleven different gait parameters and successfully showed differences among gait with multiple physical asymmetries. The results showed that mass at the distal end had a larger magnitude on overall gait asymmetry compared to leg length discrepancy. It also showed that the combined effects are varied based on the cancelation effect between gait parameters. The metric was also successful in delineating the differences of prosthetic gait and able-bodied gait at three different walking velocities.

#### Edited by:

Diego Torricelli, Consejo Superior de Investigaciones Científicas (CSIC), Spain

### Reviewed by:

Rakesh Pilkar, Kessler Foundation, United States Zhijun Yang, Middlesex University, United Kingdom

> \*Correspondence: Kyle B. Reed<EMAIL_ADDRESS>
Received: 26 September 2017 Accepted: 17 January 2018 Published: 13 February 2018

### Citation:

Ramakrishnan T, Lahiff C-A and Reed KB (2018) Comparing Gait with Multiple Physical Asymmetries Using Consolidated Metrics. Front. Neurorobot. 12:2. doi: 10.3389/fnbot.2018.00002 Keywords: gait asymmetry, leg length discrepancy, distal mass, knee orthosis, prosthetic gait

## 1. INTRODUCTION

Human gait is a complex coordinated cyclic neuromuscular process that includes voluntary and involuntary aspects (Zijlstra et al., 1995). However, this cyclic process is frequently impaired following central nervous system damage, such as stroke, or physical changes, such as wearing a prosthetic. Physical and neurological changes often result in an asymmetric gait because the person's muscles and/or control actions becomes inherently asymmetric. Typically, human gait is represented by spatiotemporal, kinematic, and kinetic parameters obtained from analyzing motion capture and force plate data (Winter, 1995). The purpose of this study is to present a simple but versatile quantitative asymmetry metric that can be used to characterize the asymmetry of gait patterns as a whole.

Gait parameters offer quantitative data that can represent a person's gait. Using a quantitative data driven analysis offers an unbiased evaluation of the effects of multiple physical asymmetries that affect human gait. For this experimental study, the physical changes were selected based on the dynamic principles related to leg length and mass on a periodic system. Under appropriate conditions two dissimilar systems can be made to exhibit synchronized motion (Handzic et al., 2015). Handzic et al. demonstrated that two double pendulums with different masses at different locations and different lengths can exhibit symmetric motion. Human legs can be modeled as double pendulum systems, which allows for a simplified explanation of their synchronized dynamics (McGeer, 1990). Discrepancies in leg lengths and lower limb amputation disrupts natural propagation and dynamics that ultimately lead to asymmetric gait patterns. Further, asymmetric effects are also observed with changes in mass, such as the addition of external mass or a prosthesis. The study presented here also includes the effect of damping and stiffness at the knee to compare a larger range of physical changes that are not limited to altering the length and mass of limbs.

## 2. BACKGROUND

Previous research about asymmetric physical changes reveal a range of different effects on a person's gait. The literature review for this study looked at various physical changes such as leg length discrepancy (LLD), the addition of mass at the distal end of the leg, amputation, and stroke. It is important to remember that although these physical changes affect every person differently, they can all be characterized using the asymmetries of biomechanical gait parameters. It is not uncommon to find similar effects on gait asymmetry with different physical changes. To illustrate these differences and similarities, this literature review also focused on prior quantitative gait metrics and the algorithms used to discern between different types of gait.

## 2.1. Gait Patterns

Approximately 0.001% of people have some form of corrective gear due to LLD (Guichet et al., 1991). LLD may cause serious long-term consequences based on several variables such as the design of corrective devices, age, weight, posture, and level of activity (Gurney, 2002). An increase of 2 cm or 3.7% in leg length difference has dramatic overall gait asymmetry, especially in vertical reaction forces during push off and initial contact (Kaufman et al., 1996). Further, LLD causes abnormal changes in foot loading patterns and increases in joint torques/moments, which could lead to long-term effects (Perttunen et al., 2004). Finally, studies have also shown that LLD causes more overall strain on the body and leads to increased expenditure of energy (Gurney et al., 2001).

Limb mass, like limb length, plays an integral role in the dynamics of human walking. Adding mass on limbs, especially toward the distal end, brings about increases in metabolic activity and disrupts spatiotemporal symmetry (Browning et al., 2007). Adding mass at the distal end has been shown to force the user to change their walking posture by moving their arms in order to maintain balance (Donker et al., 2002). These effects may cause adverse changes in walking patterns in able-bodied symmetric individuals, but the addition of weight on the non-paretic limbs of stroke victims has shown improvement in walking speed, step length, cadence, and weight bearing in the paretic limb (Regnaux et al., 2008).

Studies show that prosthetic users exhibit less effcient and unnatural gait patterns (Gitter et al., 1995; Hoffman et al., 1997). This inefficiency is more evident in transfemoral amputees than transtibial amputees, which results in the users exerting a great deal of effort to compensate for unwanted motions (Huang et al., 1979). In some cases, simple solutions can correct irregular gait. When individuals with ataxia wore a 2 lb mass on their chest, unstable motions significantly decreased and the gait was more steady and efficient (Gibson-Horn, 2008). Since amputees are physically asymmetric, bringing about efficient and symmetric gait depends on multiple factors such as length, weight of prosthesis, type of socket, length of residual limb, etc. A study on unilateral transtibial prosthetic users shows that as the mass of the prosthetic gets closer to their intact shank weight, the subjects gait becomes more asymmetric (Mattes et al., 2000).

Stroke is one of the leading causes of disability among adults, affecting ambulation, performance of activities of daily living, communication, and cognition. Physical independence with respect to walking is characterized by improvement of walking function as defined by stroke survivors (Bohannon et al., 1991). However, only a minority of people (7–22%) are able to regain sufficient function to be considered independent community ambulators post stroke (Hill et al., 1997; Lord et al., 2004).

Gait retraining post-stroke typically focuses on two main outcome measures: velocity and symmetry. Walking velocity is used as an indicator of overall gait performance and can be used to differentiate the levels of disability among the stroke patient population (Perry et al., 1995; Lord et al., 2004). A gait speed of 0.8 m/s is considered the required minimum for community ambulation (Perry et al., 1995; Bowden et al., 2008), and typically people ambulate with a mean gait velocity of 1.14 m/s (Lord et al., 2004). Gait symmetry, in contrast, is used as a measure of gait quality (Dewar and Judge, 1990; Patterson et al., 2008). Normal gait measured among able-bodied individuals was found to be fairly symmetric in spatiotemporal, kinematic, and dynamic parameters with a range of up to 4–6% asymmetry between the limbs (Herzog et al., 1989; Titianova and Tarkka, 1995).

Gait after stroke becomes asymmetric (or hemiparetic) as a consequence of altered neuromuscular signals affecting leg motor areas, typically hyper extension at the knee and reduced flexion at the hip, knee, and ankle (Brandstater et al., 1983; Wall and Turnbull, 1986; Kelly-Hayes et al., 2003). Hemiparetic gait is characterized by a significant asymmetry in temporal (e.g., time spent in double-limb support) and spatial (e.g., step length) measures of interlimb coordination (Brandstater et al., 1983; Titianova and Tarkka, 1995; Balasubramanian et al., 2007). Propulsive force of the paretic limb is also reduced compared to the non-paretic limb, as are work and power of the paretic plantar flexors (Bowden et al., 2006; Balasubramanian et al., 2007). The significant decrease in propulsive force results in smaller overall step lengths, which in turn affects the patient's gait velocity. Finally, vertical ground reaction forces (GRFs) are decreased on the paretic limb relative to the non-paretic limb (Kim and Eng, 2003), reflecting Ramakrishnan et al. Comparing Asymmetries Using Consolidated Metrics

diminished weight bearing and balancing capabilities by the paretic limb.

When an individual with an asymmetric impairment walks with symmetric step lengths, other aspects of gait become asymmetric, such as the forces in the joints (Carpes et al., 2010; Handzic et al., 2015), the amount of time spent on each leg (Kim and Eng, 2003), and other temporal variables (Sadeghi et al., 2000; Highsmith et al., 2010), all of which can be detrimental to efficiency and long-term viability. Understanding how symmetry affects function could change the fundamental nature of clinical gait rehabilitation. The results from this research could also help tailor rehabilitation treatments to target each person's specific impairment. An overall analysis of multiple gait parameters can bring equilibrium to the different, and sometimes conflicting, requirements of gait. In order to distinguish and characterize the effects of multiple gait parameters, we use metrics that consolidate and quantify the overall change in gait. This paper demonstrates the effectiveness of these quantitative gait metrics in classifying multiple physical asymmetric changes.

## 2.2. Gait Metrics

Gait metrics have been in use clinically to evaluate a subject's progress throughout their rehabilitation process. These metrics can be classified based on the type of information required, which is of two types: qualitative (Steffen et al., 2002; McConvey and Bennett, 2005) and quantitative (Schutte et al., 2000; Schwartz and Rozumalski, 2008; Rozumalski and Schwartz, 2011). Most metrics focus either on kinetics or kinematics in order to categorize various walking patterns. However, there are some that can perform the analysis utilizing both kinetic and kinematic parameters (Chester et al., 2007; Hoerzer et al., 2015). Gait metrics have also employed statistical techniques such as principle component analysis (PCA) and singular value decomposition (SVD) to reduce the dimensionality of the biomechanical parameters (Muniz and Nadal, 2009). After processing the dataset, either the Euclidean or Mahalanobis distances (Muniz and Nadal, 2009) are found, which ultimately results in the score for the metric. Previous studies used Mahalanobis distances in conjunction with PCA to analyze kinematic and specific loading at knee joints. The precursor to this research study showed that the combined gait asymmetry metric (CGAM) used a symmetry index in conjunction with Mahalanobis distances. Without the restrictions of dimensionality reduction, CGAM served as a versatile gait asymmetry metric (Ramakrishnan et al., 2016).

## 3. METHODS

In order to analyze multiple asymmetric physical changes using gait metrics, two distinct datasets were collected from eleven different types of physical alterations. The physical alterations include a prosthesis with two different sockets on an amputee, healthy individuals with eight combinations of leg length and ankle masses fitted to the non-dominant leg, and a stroke simulator. The distinct datasets for the alterations were collected on amputee and non-amputee populations. The amputee data was collected while walking at three different speeds on two types

of sockets. The data collected on able-bodied subjects includes all of the perturbations.

## 3.1. Participants

The participants for this experiment consisted of 10 able-bodied individuals and a transfemoral prosthetic user who walked with two different sockets. **Table 1** describes the subject population. Both studies were conducted under approved University of South Florida IRB protocols. The subjects provided both informed and written consents to take part in the experiments. The transfemoral amputee was selected because the subject was a high functioning transfemoral prosthetic user and can walk at speeds that are comparable to able-bodied subjects. For the data analysis, we consider the prosthetic user to be two different subjects because the change in sockets alters the subject's gait to a large extent. The study involved the subject walking at 3 different speeds using 2 different sockets: the vacuum assisted suspension (VAS) brimless socket (Klute et al., 2011) and ischial ramus containment (IRC) (Kahle, 2013).

The able-bodied individuals had no prior injuries that would alter their walking patterns. The subject's walking speeds were determined by a 10 m walk test after which the height, weight, leg length, and age of each individual participant were recorded. The participants were put through a series of randomized increments of leg lengths, addition of masses at distal end, and a combination of both effects on the same leg, which was the left leg in all cases (Muratagic et al., 2017). **Table 2** shows the various perturbations of the experiment. Finally, the stroke simulator was fitted on their dominant side, which was the right leg for all participants.

## 3.2. Experimental Apparatus

The experimental data was collected in two separate trials, one on a single amputee and one on 10 able-bodied subjects. The motion capture and force plate data was collected using the Computer Assisted Rehabilitation Environment (CAREN), which was developed by Motek Medical, Netherlands, shown in **Figure 1**. The CAREN system incorporates a ten-camera Vicon (Edgewood, NY) motion capture system, 6◦ of freedom motion

#### TABLE 2 | Experimental procedure.

base developed by MOOG, immersive 180◦ panaromic screen for virtual reality environment, split belt treadmill, and continuous force plate systems developed by Bertec.

## 3.3. Experimental Procedure

The data for the prosthetic trial was collected using 30 reflective markers, which can be seen in **Figure 2**. This marker set was used to collect extensive data on the lower and upper body dynamics of the amputee as part of another study. In this study we only use the lower limb markers out of the 30 for the gait analysis while the others are used in another analysis. The unilateral right transfemoral amputee used two prostheses, shown in **Figures 3B,C**, with different socket types, and all other components were identical. The amputee walked on both sockets at three different speeds: 0.5, 0.9, and 1.3 m/s. This was done to have a range of cadences that can represent both prosthetic and able-bodied users. The able-bodied subjects also walked at these three speeds for direct comparison. The socket systems used in the prosthesis were the IRC and VAS (Kahle et al., 2016). The IRC socket is designed to reduce pistoning and increase stability, but compromises on comfort while the VAS is designed more for comfort and aims to be dynamically efficient.

The able-bodied subjects were put through a series of 9 asymmetric changes, shown in **Figures 3D–L**, and a baseline symmetric gait, shown in **Figure 3A**. The subject's height, weight, leg length, and walking speed were recorded before beginning the experiment. The walking speed of the subject is recorded using a 10 m walk test over ground. This walking speed was the constant velocity at which the subject walked for the duration of the trials, except for the three different speeds discussed above. An 18 marker setup was used to capture the motion capture data for the able-bodied subjects. The marker setup for the lower limb is shown in **Figure 2**. The asymmetric physical changes are combinations of leg length changes and the addition of mass at the ankle. There were two levels of leg length alteration: a small height change of L1 = 27 mm and large increase of L2 = 52 mm. The small and large mass added at the distal end weighed M1 = 2.3 kg and M2 = 4.6 kg. The leg length was chosen to reflect a larger than 2 cm change in leg length which is detrimental according to literature. We used a linear relationship x and 2x to select the larger leg length. Similarly the mass was chosen based on a previous PDW study that used a linear selection method (Handz˘ic and Reed, 2013 ´ ). In addition to these changes, the subject's normal walking pattern was recorded before and after all the perturbations. The leg length and mass changes were added to the non-dominant leg of the subject to compound the asymmetric effect (Muratagic et al., 2017).

Following this trial the subject was fitted with a variable stiffness and damping knee orthotic device, which is also known as the stroke simulator (SS) (Lahiff et al., 2016). The SS is used to simulate the damping and resistance at the knee joint felt by stroke patients. The knee joint of the stroke patient has a damping effect due to the imbalance in control of the anterior and posterior femoral muscles. Stroke victims also experience stiffness/resistance to flexion of the knee joint due to the over excitation of the rectus femoris and lack of control of the posterior femoral muscles that render the knee in a constant state of extension. The device is a modified knee orthosis with a rotary damper of ζ = 8,898 g-cm-s/◦ for the damping effect and a torsional spring of K = 0.457 kg/mm for the stiffness effect. The device was fit on the subject's dominant leg. This is because the dominant limb is less coordinated and hence, exhibits the maximum asymmetric change (Sadeghi et al., 2000). The subject then walked with the SS for 10 min to adapt to the device's dynamics. Then the device was removed and the subject walks for another 2 min to measure any after effects due to the asymmetric change applied at the subject's knee.

## 3.4. Data Analysis

The motion capture and force plate data gathered from the CAREN system is used to perform the gait analysis. The gait analysis was performed using a MATLAB script that evaluates the spatiotemporal, kinematic, and kinetic parameters from the raw coordinate and force data for each perturbation. Once the parameters are analyzed, their differences are evaluated for each step using the symmetric index formula (Herzog et al., 1989). This asymmetry data is then used to obtain the Combined Gait Asymmetry Metric (CGAM) (Ramakrishnan et al., 2016), which is a single number representing an Index/score for the level of asymmetry. The study further compares the CGAM to the machine learning grouping metric with the help of LibSVM library (Chang and Lin, 2011). **Figure 4** shows the complete setup for the development of the metrics.

The CGAM is a simple metric that uses the Mahalanobis distance from ideal symmetry to the data points obtained from gait analysis. Mahalanobis distances are calculated in multi dimensional datasets such as the calculations performed on the 11 gait parameters, shown in **Figure 4**. The formula for calculating the CGAM distance is shown in Equation (1). The equation presented in this article is modified from the previous version of CGAM (Ramakrishnan et al., 2016). This formulation provides more of a weighted means approach to decrease the

spine; PSIS, posterior superior iliac spine; FEM, femur; TIB, tibia; STRN, sternum; SACR, sacral wand marker; SHO, shoulder.

subjects are secured to the safety cage of the CAREN for their safety.

variability of results for the same gait parameters. This is achieved by dividing the original equation with the summation of the inverse covariance matrix. This method eliminates an extra step of dimensionality reduction that is carried out by algorithms such as Principle Component Analysis (PCA). Although PCA can help reduce the computational burden of multi dimensional datasets, it does so at the expense of losing information. CGAM's procedure analyzes the datasets without any loss in information and provides an overall perspective of the gait asymmetry based on biomechanical parameters. Further, the multiplication of the covariance matrix provides a weighted system that allows the metric to pick up on important changes in asymmetry among all the gait parameters.

$$CGAMDistance = \sqrt{\frac{(Data) \ast in\upsilon(\Sigma) \ast (Data)'}{\sum (in\upsilon(\Sigma))}} \qquad (1)$$

## 4. RESULTS

## 4.1. Calculating the CGAM Score

To further describe how the CGAM metric combines the gait parameters into one measure, the 11 gait parameters are shown in **Figure 5** with their respective CGAM score for four of the gait alterations. An important aspect for interpreting this metric is the covariance of the asymmetry matrix, which serves to weight the measures based on how much variability is present. From Equation (1) it is clear that the covariance of the data plays a major role in calculating the Mahalanobis distances from ideal symmetry. The measures that have more variability get weighted less and more consistent measures are weighted more heavily. These weights generally account for the variations in magnitudes across all the parameters. For example, pushoff and braking forces tend to show much higher magnitude asymmetry than other measures, but they also show more variability; scaling them based on their variability makes the influence comparable to the other measures.

Even though the Stroke Simulator in **Figure 5B** looks to have low asymmetry on many measures, the variability is high on those measures. The high variability means that some steps have large asymmetry. Specifically the stroke simulator data shows a large increase in the step length and hip moment asymmetry that are

FIGURE 5 | Comparing variation of mean, standard deviation, and CGAM metric among perturbations. (A) Normal walking without any alterations, (B) walking with SS or the variable stiffness and damping knee orthosis, (C) walking with big leg length and no weight addition at the ankles, and (D) walking with big leg length and weight. SL, step length; ST, step time; GRF, ground reaction forces; PF, push off forces; BF, braking forces; KA, knee angle; AA, ankle angle; HA, hip angle; AM, ankle moment; KM, knee moment, and HM, hip moment.

part of the resultant increase in the CGAM score magnitude. In contrast a large hip moment asymmetry seen in **Figure 5C**, with large leg length increase on the left leg, does not increase the CGAM score as much since the other parameters are in the nominal range. It is important to keep in mind that the CGAM scores are measured from perfect symmetry, so even normal walking with no alteration has some asymmetry, as shown in **Figure 5A**. **Figure 5D** shows the combined overall effect of a large mass at the distal end and a large increase in leg length, which results in a larger score compared to large leg length only. Thus, the overall CGAM score is higher than the normal walking shown in **Figure 5A**, even though some of the normal walking averages are fairly asymmetric.

## 4.2. Comparison of Alterations

**Figure 6A** illustrates the CGAM scores with the alterations applied to able-bodied subjects, and **Figure 6B** illustrates the comparison of the scores between able-bodied individuals and the transfemoral prosthetic user walking with two different sockets. It can be seen in **Figure 6A** that the addition of mass at the distal end makes gait overall more asymmetric than an increase in leg length. The effect of combining mass and LLD showed that a large mass at the distal end and a small LLD had the overall highest asymmetry. This demonstrates that the largest physical asymmetry, which in this case was the large mass at the distal end combined with a large LLD, may not necessarily lead to the largest deviation in overall gait asymmetry. On closer inspection of the individual gait parameters, it was revealed that there may be a cancellation effect with the large change in leg length and hence the overall CGAM value was lower. When the subjects walked with just the larger leg length, the step lengths were more asymmetric than the step times; however, the step times were more asymmetric than step lengths when only wearing a large mass. This kind of behavior is illustrated with the different perturbations and hence, these opposite effects tend to cancel each other out which results in a lower CGAM value. A previous study conducted by Muratagic et al. (2017) found that there were no significant effects observed due to the combination of LLD and distal. However, the study also observed some cancellation effects due to the combination of LLD and mass which showed that there are potential combinations that could result in a balanced gait pattern.

The changes related to prosthetics also had significant effects, as shown in **Figure 6B**. Wearing the SS affected the gait of all able-bodied subjects and caused a similar level of asymmetry in this metric compared to an amputee wearing a prosthetic. However, speed affected the gait asymmetry, and there was one speed on each of the prostheses that the subject was not comfortable with. Another observation from **Figure 6B** is that the IRC socket is more consistent in overall gait asymmetry but the subject felt less pain using the VAS socket, and gait with the VAS has a better overall gait at a high velocity (Kahle et al., 2016).

## 4.3. Statistical Analysis

A two-way repeated measures ANOVA analysis was performed with mass and leg length as independent variables and CGAM as the dependent variable. Mauchly's Test indicates that sphericity was not violated. The results of the ANOVA show that distal mass, F(2, 18) = 19.15, p < 0.005, and leg length, F(2, 18) = 5.72, p < 0.05, show statistically significance results in regards to CGAM. There was not a statistically significant interaction between the amount of mass added and amount of added leg length, F(4, 36) = 0.20, p = 0.49. This is similar to the effects observed in our lab's previous study (Muratagic et al., 2017). Further, the post-hoc comparisons for mass revealed significant difference between no mass and both small and large mass conditions. There was a statistically significant difference between no length and the large leg length condition. This analysis method matches our previous study, and the conclusions are similar. However, this analysis excludes the stroke simulator and different speeds, so an additional one-way ANOVA was performed.

A one-way repeated measures ANOVA analysis was performed with all 14 of the gait patterns shown in **Figure 6A** used as independent variables and CGAM as the dependent variable. This analysis was performed to examine the individual differences of all the gait patterns, unlike the two-way ANOVA

describe above that focused only on the added mass and height. Mauchly's Test indicates that sphericity was not violated. The results of the ANOVA show that there were a statistically significant differences in gait patterns, F(13, 117) = 10.21, p < 0.0001. The post-hoc test results are shown in **Figure 6A**. The normal gait pattern is statistically significant to the perturbation with large leg length and small mass and the gait pattern with the stroke simulator. Similarly, gait pattern with the subject walking at 1.3 m/s showed statistical significant difference between perturbation with large mass and gait with stroke simulator. This was to be expected since 1.3 m/s is close to the average self selected speed of all the subjects.

prosthetic user at three different speeds (0.5, 0.9, and 1.3 m/s).

## 4.4. Comparison to Machine Learning

Machine learning has been used in data driven industries to find patterns in large amounts of disparate datasets. The two datasets that were collected during this study represent gait with multiple asymmetric changes and hence, can be used to find patterns. For this study the LibSVM library (Chang and Lin, 2011) was used because it is easy to implement and it is widely used for research data. The machine is trained using labels and a training dataset. The labels are long vectors with a single number and the training datasets are ground truths. In the case of this study the labels were 0 and 1. Label 0 was used for the perfect symmetry which is a zero matrix with 11 columns and multiple rows. Label 1 was used for training asymmetry data. **Figure 7** shows the results of grouping predictions from 2 different asymmetry training datasets.

The pattern of the LibSVM grouping Index seen in **Figure 7A** is very similar to the pattern of the CGAM Mahalanobis distance in **Figure 6A**. Although the specific values cannot be compared directly because the modes by which they arrive at the results are inherently different, the trends highlight the differences between these two methods. The CGAM metric uses a simple Mahalanobis distance calculated from ideal symmetry while the more complex machine learning metric groups the data based on training datasets. LibSVM is not as reliable at this stage for being considered as a gait asymmetry metric because, based on the training datasets, the results vary substantially. This can be seen by comparing **Figure 7A,B** where the training datasets were different and the grouping predictions are completely different. This can be attributed to the different asymmetries present in the SS data and the weight/LL datasets. CGAM does not get affected by these differences and offers a more objective metric that can be used to classify the asymmetric changes. Another problem with Machine Learning as a metric is the requirement of large datasets.

## 5. DISCUSSION

This study demonstrates a simple metric that can help classify physical changes in human gait using the asymmetries of gait parameters. The results discussed above show that the metric is able to successfully categorize the extent of asymmetric changes caused by different perturbations. For example the CGAM scores for walking with the SS, which is designed to cause asymmetric gait, has a significantly larger value compared to the value that was gathered for gait immediately after the device was taken off. The after-effects of the SS are also more asymmetric than a normal gait pattern, which shows that the individuals adapted to the SS. Classification of gait based on overall symmetry will help clinicians keep track of a subject's progress, such as preand post- physical therapy regiments. The SS can be examined as an impeding exoskeleton. Hence, the gait wearing the SS and after removing the SS are both asymmetric overall. Conversely, in robot-assisted locomotion therapy, the outcomes are expected to be more symmetric (Lo et al., 2010). CGAM could provide researchers the tools to measure the overall change in gait asymmetry and modify their rehabilitation techniques to induce better gait patterns. This approach is different from prior research practices that limited their study to either spatio-temporal, kinematic, or kinetic data.

Another approach is analyzing an individual's gait parameters separately. This method could reveal insights on specific comparisons, but the complexity increases with the number of gait parameters. It is difficult to determine if the gait has improved when separately examining 11 parameters. The CGAM could make this evaluation easier since it can be used to represent a range of gait parameters, and it is not just limited to the 11 parameters that were used in this study. The subsets of the gait parameters can be made to fit the requirements of the clinicians such as reporting on improvements in only spatiotemporal parameters or only in kinematics. For example, in a prior study with CGAM, only 5 gait parameters were used to analyze the data (Ramakrishnan et al., 2016). The parameters were step length, step time, vertical forces, push off forces, and braking forces. Using these 5 asymmetry parameters, the CGAM was able to classify the different perturbations of leg length and addition of masses on separate legs. Although this metric used 11 gait parameters, the two-way ANOVA showed similar results to the analysis performed using five gait parameters in the study by Muratagic et al. (2017). This leads to one of the avenues for future research which involves determining the minimum gait parameters required to represent a gait pattern. CGAM is designed to be used for any number of gait parameter asymmetries representing multiple forms of data. However, many research studies typically do not come equipped with a CAREN or similar system to gather large amounts of data. One of the advantages of CGAM is that it can be potentially used on limited availability of quantitative asymmetric data. We are exploring the boundaries of this metric to be able to benchmark it for standard protocols for gait analysis.

Consolidated metrics such as CGAM and Machine Learning offer a unique and simplified perspective into categorizing gait data between multiple asymmetric datasets. CGAM has the potential of serving as a benchmark in representing overall gait asymmetry using multiple different parameters. The multidimensionality that CGAM offers makes it versatile and as shown in this article we can assess multiple gait patterns with different causations. These metrics have to be field tested in clinical trials in order to be formally proposed for clinical use. It is important to remember that these metrics could direct researchers to help patients achieve a well rounded gait. A well rounded gait can be characterized as a sustainable gait that an individual adopts that has the least overall asymmetry, not just a decrease in one parameter. Some parameters would remain asymmetric so that other parameters could become closer to symmetry. In case of a person who is physically asymmetric, this would mean adopting a gait and posture that will have a balance between all the gait parameters. This adaptation of a well rounded gait will help a physically impaired person to sustain a long-term gait that may not necessarily be as symmetric as an able-bodied gait, but it is subjectively beneficial to their specific physical asymmetry. A well rounded gait will alleviate long-term problems caused by asymmetric forces and moments acting on the person's body.

In this study the 11 parameters were chosen because they represent important gait parameter information and have clear symmetry values between each limb. With both metrics it is clearly seen that the addition of mass at the distal end has a larger effect on the overall symmetry than leg length discrepancies.

The combined effect of leg lengths and mass addition did not reveal a clear pattern but the results were as expected in most cases. For example, the combination of big leg length and mass had a slightly larger effect than small mass and leg length. However, the combination of a small leg length and big mass had a lot more deviation than big leg length and big mass. This is caused by the cancellation effects between gait parameters, which in turn resulted in a larger or smaller CGAM value.

There are some limitations associated with this method and study. There are many other gait patterns that were not discussed that arise from other gait impairments that should also be evaluated. Future studies can easily incorporate this metric in their analysis to compare the individual metrics to an overall picture of the gait. This will help in the generalization of this concept and also help to make the comparisons across different gait patterns more meaningful. This metric could also be optimized to find the most salient gait parameters to include; some of the ones used in this study may not be ideal and there may be others that are more beneficial to include.

The prosthetic gait at the three different speeds showed that the overall symmetry improves with increases in speed. It has been shown in literature that amputees achieve better spatiotemporal and kinematic symmetry at higher speeds, but at the expense of kinetic symmetry which can cause long-term degeneration effects (Nolan et al., 2003). We require a bigger patient population in order to gather all variations of prosthetic gait and leave that to future studies. The analysis provided in this study will improve further and can be more robust if a larger dataset from multiple patient population is used. The study presented in this article provides some proof into the efficacy of CGAM but it is limited by the small population size.

The CAREN is a versatile device that was used to collect all the data for this study and has been used in other similar studies (Ramakrishnan, 2014; Muratagic, 2015). To further understand the effects and dynamics of physical asymmetries, the split belt treadmill can be used to exaggerate asymmetries. Split belt treadmills are used to rehabilitate gait affected by hemiplegia by having the treads move at different velocities. This exaggeration of hemiplegic gait temporarily restores the person's gait closer to symmetry. However, successfully returning a person's gait to spatio-temporal symmetry does not necessarily guarantee an overall effective gait with a healthy ratio of symmetry between all gait parameters. To further explore how physical asymmetries combine, the split belt treadmill could be used in conjunction with an added mass and/or LLD.

## 6. CONCLUSION

Analyzing multiple physical asymmetries in one platform requires a special form of metric. This is because every perturbation of physical change that impairs an individual's gait has to be accounted for and kept track of following clinical procedures. The consolidated metrics such as CGAM and Machine learning can be quantitative data analysis tools that can help researchers keep track of a person's overall gait asymmetry. These metrics can be obtained using all gait asymmetry parameters such as spatio-temporal, kinematic, and kinetic or by using subsets and combinations of any or all of these parameters. This versatile platform allows researchers to have many options for generating metrics to represent the progress or regression of an individual over a period of training and time.

## AUTHOR CONTRIBUTIONS

TR designed and performed the experiments and analyzed the results. C-AL designed and tested the stroke simulator. C-AL also helped TR with the experiments and data analysis. KR advised C-AL and TR throughout the design, execution, and analysis. All authors participated in writing and revising the article.

## FUNDING

This material is based upon work supported by the National Science Foundation under Grant Number IIP-1602020 and the American Orthopedic and Prosthetic Association (AOPA).

## REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Ramakrishnan, Lahiff and Reed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

## Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in able-Bodied Individuals

*Blair Hu1,2\*, Elliott Rouse3 and Levi Hargrove1,2,4*

INTRODUCTION

*1Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States, 2Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States, 3Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States, 4Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States*

Keywords: gait, locomotion, biomechanics, electromyography, benchmark

## *Edited by:*

*Diego Torricelli, Consejo Superior de Investigaciones Científicas (CSIC), Spain*

#### *Reviewed by:*

*Jan Veneman, Tecnalia, Spain Antonio J. del-Ama, National Hospital for Paraplegics, Spain Cristiano De Marchis, Università degli Studi Roma Tre, Italy*

> *\*Correspondence: Blair Hu<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Bionics and Biomimetics, a section of the journal Frontiers in Robotics and AI*

> *Received: 25 September 2017 Accepted: 30 January 2018 Published: 19 February 2018*

#### *Citation:*

*Hu B, Rouse E and Hargrove L (2018) Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals. Front. Robot. AI 5:14. doi: 10.3389/frobt.2018.00014*

The field of assistive robotics has experienced rapid growth in the number of and capabilities of wearable lower limb assistive devices, which include robotic exoskeletons, orthoses, and prostheses. These devices have shown promising potential to restore motor function to individuals with gait impairments by providing locomotion assistance. Although many devices have already demonstrated impressive performance in a variety of real-world conditions, comparing their performance objectively and improving their controllability remain challenging for several reasons. First, the outcome measures (e.g., joint kinematics, metabolic cost, clinical scores, and prediction accuracy) used by studies demonstrating improved walking ability with an assistive device are not consistent. Second, many studies only use treadmill walking or do not collect data from a variety of locomotor activities due to constraints in a device's mechatronic design and/or control system. Third, many devices are in the process of commercialization, so testing data are seldom shared with the research community. In addition, many devices implement their own unique control frameworks that are not generic enough to conveniently implement on other hardware. Therefore, we expect improving access to device-agnostic neuromechanical signals during walking-related activities (from which researchers could develop and test novel control strategies before implementation on hardware) will be valuable to the field of wearable lower limb assistive devices.

Meanwhile, many benchmarks for the biomechanics of able-bodied human locomotion without an assistive device have already been established, some of which are publicly available. The gold standard for high-resolution biomechanical gait analysis is marker-based optical motion capture with ground reaction force measurement. Decades ago, seminal work from Winter (1983) used these techniques to introduce an inter-subject biomechanical analysis of level ground walking (LW) at different speeds. Their normative gait dataset includes electromyography (EMG) and joint kinematic and kinetic patterns and has since been expanded by other researchers to include more subjects and strides (e.g., Kadaba et al., 1990; Kirtley, 2014). The steady-state biomechanics of other common locomotor activities such as ascending and descending stairs and sloped surfaces of different geometries have also been reported in separate studies using similar techniques but these data are not as accessible to researchers (e.g., McFadyen and Winter, 1988; Riener et al., 2002; Lay et al., 2006, 2007; Protopapadaki et al., 2007; Franz et al., 2012).

Human locomotion is most accurately quantified by joint kinematics, kinetics, and EMG using traditional laboratory-based instrumentation and techniques developed for biomechanical gait analysis. However, the exciting potential of wearable lower limb robotics lies in its promise to bring these devices closer to everyday life, where alternative techniques are required to more ubiquitously measure neuromechanical signals during walking-related activities. Methods to more freely measure human movement have been developed in the field of human activity recognition (HAR), which aims to use continuous streams of sensor data to recognize and monitor common activities of daily living such as sleeping, walking, exercising, and manipulating objects. As a result, HAR has produced an abundance of publicly available datasets. These repositories are valuable because they contain many different types of activity information from many subjects; however, they are not very suitable for more systematic characterization of normal locomotion. Sometimes, HAR datasets are collected from impaired populations or during more natural, but complex combined movements for which the ground truth activity is more ambiguous. Some datasets are collected using minimal instrumentation (e.g., smartphone only), which is convenient but incomplete. By contrast, others rely on non-portable instrumentation (e.g., optical motion capture or video), which is highly accurate but not representative of biomechanical signals accessible for controlling a device in a more ecological setting. Also, many only contain single modalities (e.g., kinematics but no EMG) and/or use lower sampling rates that may be insufficient for certain online control schemes.

To the best of our knowledge, there still does not exist a publicly available database of kinematic and EMG data simultaneously recorded from wearable sensors as able-bodied individuals freely transition between several distinct locomotor activities. To address some of these aforementioned limitations and provide relevant reference data for researchers in the field of wearable lower limb assistive devices, we introduce a device-agnostic benchmark dataset of bilateral neuromechanical signals called ENcyclopedia of Able-bodied Bilateral Lower Limb Locomotor Signals (ENABL3S). The dataset contains bilateral EMG and joint and limb kinematics recorded from wearable sensors for 10 able-bodied individuals as they freely transitioned between sitting, standing, and several walking-related activities [level ground, stair ascent (SA)/stair descent (SD), and ramp ascent (RA)/ramp descent (RD)]. Although these data are not intended to replace existing benchmarks for biomechanical gait analysis, we believe they still fill a gap between those benchmarks and HAR datasets by providing richer neuromechanical data collected from wearable sensors using a unified protocol for several distinct locomotor activities. In this data report, we summarize our methods for instrumenting subjects, collecting data, and post-processing for artifact removal and gait segmentation. We also present a summary of the types of locomotor activities and transitions captured by our protocol, validate our results, and conclude with suggestions for how other researchers in the field may benefit from this dataset.

## MATERIALS AND METHODS

## Instrumentation Setup

Ten healthy able-bodied subjects (seven male, three female; 25.5 ± 2 years; 174 ± 12 cm; 70 ± 14 kg) without any gait

impairments were recruited and completed the following protocol between January and February 2017. Before walking, subjects were instrumented with wearable sensors to measure bilateral lower limb muscle activity and joint and limb kinematics. EMG signals were recorded using bipolar surface electrodes (DE2.1; Delsys, Boston, MA, USA) from the same seven muscles in each leg: tibialis anterior (TA), medial gastrocnemius (MG), soleus (SOL), vastus lateralis (VL), rectus femoris (RF), biceps femoris (BF), and semitendinosus (ST). These muscles were chosen because they are in part responsible for hip and knee flexion/extension and ankle plantarflexion/dorsiflexion, movements that are commonly assisted by wearable devices. They are also relatively easy to target when facing the subject from in front and behind. The muscle sites were prepared by removing excess hair, and the skin was cleaned by mildly scrubbing with an alcohol wipe. Sensors were attached to the skin with a double-sided adhesive. Electrode placement was guided by palpation according to the Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles standards and verified by having subjects perform maximum voluntary contractions (MVC). Subjects performed three repetitions of ankle dorsiflexion/plantarflexion and knee flexion/extension for both legs. EMG signals were amplified by 1,000×, hardware band-pass filtered between 20 and 450 Hz (Bagnoli 16, Delsys), and sampled at 1 kHz.

Joint kinematic signals (sagittal plane only) were recorded using electrogoniometers (SG150; Biometrics Ltd., Newport, UK) placed on the knee and ankle and sampled at 500 Hz. At the beginning of trials, the goniometers were zeroed while the subject was in the upright standing position. 6-DOF (tri-axial accelerometer and gyroscope) inertial measurement units (IMUs) were placed bilaterally on the subjects' thigh (below RF) and shank (adjacent to TA) and sampled at 500 Hz (MPU-9250; Invensense, San Jose, CA, USA). Goniometers and IMUs were secured to the subject using a combination of double-sided adhesive, elastic straps, and Coban self-adherent wrap. Another IMU was placed in a custom manufactured holster (tilted 20° from vertical) and worn around the waist with a belt. All signals were simultaneously recorded with a custom 16-bit data acquisition device that permitted multi-rate sampling. To facilitate integration with our custom data acquisition software, all wearable sensors were used in a tethered setup; as a drawback, fully instrumenting each leg took up to an hour. The full instrumentation setup with IMU orientations is shown for a representative subject in **Figure 1**.

## Data Collection Protocol

In an experimental session, each subject was barefoot and completed approximately 25 repetitions of a circuit consisting of sitting (S), standing (St), LW, ascending/descending a ramp with a 10° slope (RA/RD), and ascending/descending a four-step staircase (SA/SD) step-over-step. These activities were chosen because they encompass the different types of terrain likely encountered in community ambulation and were completed as a circuit in a 20 ft. × 30 ft. room for practicality and for increasing the number of repetitions. A platform (30″ tall) joined the staircase (7.75″ rise, 10″ run) and ramp (14 ft. long) to allow all possible transitions between these activities. Data from each circuit were divided into

two segments and recorded as separate trials. Odd-numbered trials consisted of S → St → LW → SA → LW → RD → LW → St → S. Even-numbered trials consisted of S → St → LW → RA → LW → SD → LW → St → S. The total distance walked for each continuous segment was approximately 45 ft. Trials during which sensors needed to be repositioned or the tether became tangled were excluded. Subjects were instructed to freely transition between locomotor activities at their self-selected speed, and breaks were routinely administered to avoid fatigue. The experimenter labeled the true locomotor intent of the subject using a key fob. Data collection took up to 2 h.

## Post-processing

Heel contact and toe off gait events for each leg were reliably identified by finding peaks in the mean-subtracted and low-pass filtered (first-order Butterworth, 6 Hz) sagittal plane angular velocity (GY) of the shank segment using a threshold-based method similar to Maqbool et al. (2016). Briefly, the largest peaks in angular velocity were first used to identify mid-swing events. Toe off events were identified by searching for peaks before each mid-swing event. Heel contact events were identified by searching for peaks after each preceding mid-swing event. Event switches were initially placed beneath the heel and first metatarsal of each foot, but they triggered many false negatives and positives in our setup perhaps due to mechanical wear and/or foot placement on the staircase. Therefore, they were only used for validating the IMU-based segmentation technique. Gait events corrupted by motion artifacts (i.e., pauses and trips) were excluded. EMG signals were high-pass filtered (sixth-order Butterworth) at 20 Hz, low-pass filtered (sixth-order Butterworth) at 350 Hz, and notch-filtered (sixth-order Butterworth, 6 Hz width) at 60, 180, and 300 Hz to attenuate motion artifact and ambient interference. Goniometer and IMU signals were low-pass filtered (sixth-order Butterworth) at 10 and 25 Hz, respectively. Joint velocities were indirectly computed by taking the central-difference numerical derivative of the joint position and added to the goniometer channels.

All signals were segmented into analysis windows beginning 300 ms before each identified heel contact or toe off gait event. Four additional 300 ms analysis windows near each identified gait event (delayed by 30, 60, 90, and 120 ms relative to each event) were used. For each window, we extracted features previously used in intent recognition for control of a powered knee-ankle prosthesis. Features for goniometer and IMU channels included the mean, SD, maximum, minimum, initial, and final values (Varol et al., 2010) (six features/channel). Features for EMG signals included the mean absolute value, waveform length, number of zero crossings and slope sign changes, and the coefficients of a sixth-order autoregressive model (Huang et al., 2005; Hargrove et al., 2008) (10 features/channel). There were a total of 23 sensors (14 EMG, 4 goniometer, 5 IMU), 52 channels (14 EMG, 8 goniometer, 30 IMU), and 368 features (140 EMG, 48 goniometer, 180 IMU).

## RESULTS

The data are saved in CSV format in subject-specific folders and are available to download from Figshare at https://doi.org/10. there is a metadata file, which catalogs the filenames, summary statistics (mean, SD, minimum, maximum) of each goniometer channel, and signal-to-noise ratios [ratio of maximum to baseline root-mean-square (RMS) voltage] of each EMG channel for each circuit. Subject-specific folders also include folders for the raw and processed data from individual circuits, a folder containing the processed EMG signals from all muscles during MVC trials, and a folder containing the features extracted from the five different 300 ms analysis windows (beginning 300, 270, 240, 210, and 180 ms before the gait events identified for each leg). Data from individual circuits also contain columns specifying the label of the true locomotor activity, the indices of heel contact and toe off gait events, and four-digit triggers denoting the outgoing and incoming locomotor activities and gait phases. The first row of each file is a header specifying the column order. The post-processed data from all trials are included for completeness although some trials 6084/m9.figshare.5362627. Within each subject-specific folder, include disturbances (e.g., pauses, trips, and missed transitions), which are noted in the metadata file. However, only gait events from disturbance-free segments of trials are reported and used for feature extraction. Feature data also contain columns specifying the corresponding leg-phase (1, right heel contact; 2, right toe off; 3, left heel contact; 4, left toe off) and the four-digit trigger. The first row of the feature data is a header specifying the column order. Ipsilateral refers to the side in which the gait event was detected (e.g., the right leg for right heel contact and right toe off events).

The overall composition of ENABL3S is shown in **Table 1**. For each subject, there were 530 ± 46 heel contact events and 536 ± 45 toe off events for each leg (mean ± SD) after excluding transitions to or from standing. Additional subject information and an explanation of nomenclature and numbering are also included on Figshare.

## DISCUSSION

ENcyclopedia of Able-bodied Bilateral Lower Limb Locomotor Signals represents a benchmark of bilateral lower limb neuromechanical signals recorded from able-bodied individuals using wearable sensors during unassisted locomotion. The purpose of introducing this dataset is not to replace existing benchmarks for biomechanical gait analysis of steady-state locomotion but to provide a publicly available set of rich biomechanical data from wearable sensors, representing a compromise between traditional techniques and methods from HAR. ENABL3S includes data from several distinct walking-related activities (with transitions), which we expect to be helpful for understanding patterns in normal locomotion and developing novel control strategies for wearable lower limb assistive devices.

In order to assess the validity of this dataset, we chose to compare our recordings (averaged across legs and all subjects) to previously reported biomechanical measurements of level

Table 1 | Characteristics of ENcyclopedia of Able-bodied Bilateral Lower Limb Locomotor Signals.

*The total number and proportion of gait events belonging to each type of locomotor activity are aggregated across all subjects.*

walking because these data are most accessible. Due to movement out of the sagittal plane and skin deformation/relative motion of the ankle goniometer, our measurements of ankle position were not considered biomechanically accurate signals. Nonetheless, these signals may still be useful for developing control strategies because many devices do not reproduce physiological motion and/or use embedded joint encoders to sense relative ankle position. However, our measurements of knee position were more accurate when compared to previously reported data recorded using optical motion capture (Winter, 1983; McClelland et al., 2011). The RMS error between ENABL3S and Winter (1983) was 5.9 and 9.5° for stance and swing phases, respectively. The *R*2 values were 0.73 and 0.94 for stance and swing phases, respectively. Our reported values for knee range of motion (ROM) during stance and swing phases (flexion at initial contact: 10.9 ± 5.6°; stance ROM: 6.8 ± 5.2 to 26.9 ± 5.7°; swing ROM: 3.4 ± 5.4 to 58.0 ± 6.5°) were also comparable to reported values (McClelland et al., 2011). Errors in position can be attributed to a combination of differences in walking speed, minor misalignment of the sensor with the axis of rotation, and skin deformation/relative motion. The knee position could also be estimated (perhaps more accurately) by subtracting the orientations of the shank and thigh IMU sensors. The patterns of EMG activation for ankle plantarflexor/dorsiflexor and knee flexor/extensor muscles were also qualitatively similar to those previously reported for unassisted overground walking at self-selected speed (Winter, 1983; Sylos-Labini et al., 2014). Knee position and EMG from TA, MG, BF, and VL aggregated across legs for all steady-state level walking steps for all subjects can be found in a supplementary document on Figshare. By confirming the accuracy of our measured kinematic and EMG signals, we also validate the IMU-based method for gait segmentation.

Although these data are not as high resolution as optical motion capture, they strike a balance between resolution of signals, breadth of activities represented, feasibility for online control schemes, and contribution to existing publicly available datasets for human locomotion. These data can be used for developing novel control strategies such as intent recognition (i.e., predicting future states based on signals detected before movement completion) and more specifically investigating sensor fusion techniques and machine learning approaches for feature extraction and classification (e.g., deep learning). These data can also be interpreted as a simulation of able-bodied individuals walking with a completely massless and transparent (i.e., perfectly backdrivable) device and can be used to derive a device-agnostic upper bound on control strategies such as intent recognition. The raw data reported here may also be useful for comparing the performance of alternative control systems, assessing inter-subject variability, and comparing user-based biomechanical signals collected from an impaired population or a population walking with an assistive device (e.g., knee orthosis and ankle-foot prosthesis) to unassisted normal locomotion.

## ETHICS STATEMENT

This study was carried out in accordance with the recommendations of the Northwestern University Institutional Review Board with written informed consent from all the subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Northwestern University Institutional Review Board.

## AUTHOR CONTRIBUTIONS

BH helped in conceiving the study concept, collecting, analyzing, and interpreting the data, and drafting the manuscript. LH and

## REFERENCES

ER helped in conceiving the study concept and interpreting the data, critically revising the manuscript for important intellectual content, obtaining funding, and supervising the study. All the authors read and approved the final manuscript.

## FUNDING

This research was supported by USSOCOM Contract No. H92222-16-C-0111.

total knee replacement. *Knee* 18, 151–155. doi:10.1016/j.knee.2010. 04.005

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2018 Hu, Rouse and Hargrove. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Corrigendum: Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals

#### Blair Hu1,2 \*, Elliott Rouse3 and Levi Hargrove1,2,4

**A Corrigendum on**

1 Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States, 2 Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States, 3 Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States, 4 Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States

#### Keywords: gait, locomotion, biomechanics, electromyography, benchmark

#### Edited and reviewed by:

Diego Torricelli, Consejo Superior de Investigaciones Científicas (CSIC), Spain

### \*Correspondence:

Blair Hu<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Biomedical Robotics, a section of the journal Frontiers in Robotics and AI

Received: 12 September 2018 Accepted: 26 October 2018 Published: 20 November 2018

#### Citation:

Hu B, Rouse E and Hargrove L (2018) Corrigendum: Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals. Front. Robot. AI 5:127. doi: 10.3389/frobt.2018.00127

## **Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals**

by Hu, B., Rouse, E., and Hargrove, L. (2018) Front. Robot. AI 5:14. doi: 10.3389/frobt.2018.00014

In the original article, there were two errors. In the text, the abbreviation for semitendinosus was omitted. In the text, the URL to the data repository available on Figshare was also incorrect.

Corrections have been made to Materials and Methods, Sub-section Instrumentation Setup, Paragraph one and Results, Paragraph one.

EMG signals were recorded using bipolar surface electrodes (DE2.1; Delsys, Boston, MA, USA) from the same seven muscles in each leg: tibialis anterior (TA), medial gastrocnemius (MG), soleus (SOL), vastus lateralis (VL), rectus femoris (RF), biceps femoris (BF), and semitendinosus (ST).

The data are saved in CSV format in subject-specific folders and are available to download from Figshare at https://doi.org/10.6084/m9.figshare.5362627.

The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Hu, Rouse and Hargrove. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Automatic Setting Procedure for Exoskeleton-Assisted Overground Gait: Proof of Concept on Stroke Population

Marta Gandolla1 \*, Eleonora Guanziroli 2 , Andrea D'Angelo1 , Giovanni Cannaviello2 , Franco Molteni 2 and Alessandra Pedrocchi 3

1 Nearlab@Lecco, Polo territoriale di Lecco, Politecnico di Milano, Lecco, Italy, 2 Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Italy, 3 NearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy

Stroke-related locomotor impairments are often associated with abnormal timing and intensity of recruitment of the affected and non-affected lower limb muscles. Restoring the proper lower limbs muscles activation is a key factor to facilitate recovery of gait capacity and performance, and to reduce maladaptive plasticity. Ekso is a wearable powered exoskeleton robot able to support over-ground gait training. The user controls the exoskeleton by triggering each single step during the gait cycle. The fine-tuning of the exoskeleton control system is crucial—it is set according to the residual functional abilities of the patient, and it needs to ensure lower limbs powered gait to be the most physiological as possible. This work focuses on the definition of an automatic calibration procedure able to detect the best Ekso setting for each patient. EMG activity has been recorded from Tibialis Anterior, Soleus, Rectus Femoris, and Semitendinosus muscles in a group of 7 healthy controls and 13 neurological patients. EMG signals have been processed so to obtain muscles activation patterns. The mean muscular activation pattern derived from the controls cohort has been set as reference. The developed automatic calibration procedure requires the patient to perform overground walking trials supported by the exoskeleton while changing parameters setting. The Gait Metric index is calculated for each trial, where the closer the performance is to the normative muscular activation pattern, in terms of both relative amplitude and timing, the higher the Gait Metric index is. The trial with the best Gait Metric index corresponds to the best parameters set. It has to be noted that the automatic computational calibration procedure is based on the same number of overground walking trials, and the same experimental set-up as in the current manual calibration procedure. The proposed approach allows supporting the rehabilitation team in the setting procedure. It has been demonstrated to be robust, and to be in agreement with the current gold standard (i.e., manual calibration performed by an expert engineer). The use of a graphical user interface is a promising tool for the effective use of an automatic procedure in a clinical context.

Keywords: lower-limb exoskeleton, electromyography, automatic calibration, neurorehabilitation, therapy personalization

#### Edited by:

Diego Torricelli, Consejo Superior de Investigaciones Científicas (CSIC), Spain

#### Reviewed by:

Giacomo Severini, University College Dublin, Ireland Iolanda Pisotta, Fondazione Santa Lucia (IRCCS), Italy

> \*Correspondence: Marta Gandolla<EMAIL_ADDRESS>
Received: 29 September 2017 Accepted: 20 February 2018 Published: 19 March 2018

#### Citation:

Gandolla M, Guanziroli E, D'Angelo A, Cannaviello G, Molteni F and Pedrocchi A (2018) Automatic Setting Procedure for Exoskeleton-Assisted Overground Gait: Proof of Concept on Stroke Population. Front. Neurorobot. 12:10. doi: 10.3389/fnbot.2018.00010

## INTRODUCTION

Stroke is the leading cause of long-term disability in adults despite the advances achieved in the management of its acute phase (Heiss and Kidwell, 2014; Tacchino et al., 2017). Independent walking in particular has been associated to an increase in patients' ability to perform daily life activities and self-esteem. Although more than half of patients achieve an independent walking, this achievement may not be functional to carry out activities of daily living. Locomotion is defined as a cyclical lower limbs activity that results from intricate dynamic interactions between a central program (at brain and spinal cord level) and feedback mechanisms from muscles, tendons, and skin afferences, as well as vision, audition, and vestibular senses (Rossignol et al., 2006). The lower limb neuromuscular pattern should compensate body weight support, provide forward and lateral stability, and forward progression to ensure intra and inter-limb multi-joints coordination (Perry and Burnfield, 2010). Common stroke-related locomotor impairments (e.g., imbalance, gait asymmetry, poor inter-limb coordination) are often associated with abnormal timing and intensity of recruitment of the affected and non-affected lower limb muscles.

Timing and intensity of muscles recruitment influence kinematic and kinetic pattern of lower limbs and intra and inter-limb coordination (Mulroy et al., 2003; Den Otter et al., 2007). Restoring the coordination in muscles activation of lower limbs is a key factor to facilitate recovery of gait capacity and performance, and to reduce maladaptive plasticity in stroke patients. Evidence within the last 20 years has shown that an injured central nervous system has the ability to reorganize after damage (Nudo, 2013; Gandolla et al., 2016). The reorganization is dependent on motor activity executed during rehabilitative training, and is followed by functional improvements (Edgerton et al., 2004; Maier and Schwab, 2006; Gandolla et al., 2014, 2016). In order to achieve better outcomes in stroke survivors, gait rehabilitation should target impairments in coordination and allow to augment the number of repetitions during walking practice (Eng and Tang, 2007).

Nowadays, wearable lower limbs powered exoskeletons may be a valuable adjunctive rehabilitation therapy aiming at augmenting training dose with repeatable, task-oriented, and controlled movements, as suggested by the principles of motor learning (Dietz and Harkema, 2004). In fact, as a common approach implemented in lower limbs exoskeleton commercial devices, the devices include actuators that support patient's legs through the gait cycle in the sagittal plane (e.g., Lokomat, Hocoma; ReWalk, ReWalk Robotics). The robotic device guides the legs through pre-programmed physiological gait patterns this kind of therapeutic intervention is fairly new for stroke patients, however preliminary findings suggest that exoskeletal gait training is equivalent to traditional therapy for chronic stroke patients, while sub-acute patients may experience added benefit from exoskeletal gait training (Louie and Eng, 2016). Ekso is a wearable powered exoskeleton robot able to support stroke patients during over-ground gait training. The kinematic chain of the exoskeleton reproduces the human lower limbs walking pattern. In addition, Ekso actuators control patient's legs through the gait cycle in the sagittal plane. Ekso can be used as a therapeutic device in patients who must re-learn walking with a proper step pattern and functional weight shift by moving the patient's legs through a customizable predefined patient-tailored kinematic pattern. Ekso allows different setting for each patient in terms of swing velocity, step length, lateral shift. In this way, it is possible to control the walking pattern in terms of gait cycle timing (i.e., stance vs. swing phase duration), inter-limb and inter-joint coordination, lateral shift, trunk-lower limb angle, and timing to achieve appropriate limb loading.

The fine-tuning of the exoskeleton control system is crucial, and it is set according to the residual functional abilities of the patient. The interaction between exoskeleton and the patient can be seen under two different aspects: physical Human–Robot Interaction and cognitive Human–Robot Interaction (Pons, 2010; Lee et al., 2012). Physical Human–Robot Interaction includes the generation of supplementary forces to overcome human physical limits. In the case of the present study, the patient triggers each step, which however follows a predefined fully supported physiological trajectory. The interaction is therefore devoted to the generation of a proper gait cycle. Cognitive Human–Robot Interaction highlights the possibility to maintain the control of the robot from the human. In this study, the patient has the direct control on the trigger of each step though body lateral shift. Given the use of a commercial device, both aspects of Human-Robot Interaction depends on robotic device proper setting—the fine-tuning procedure is necessary to ensure the best power transfer between subject and robot. Surface ElectroMyoGraphy (sEMG) of the key muscles controlling multijoints coordination of lower limbs is an effective way to noninvasively define motor control during spontaneous over-ground gait.

This work focuses on the definition of an automatic calibration procedure able to detect the best Ekso setting for each patient. Ekso setting has been defined using the neuromuscular pattern of the lower limbs collected with the superficial EMG in hemiparetic stroke patients. The proposed approach for an automatic calibration procedure is based on the hypothesis that the best Ekso setting yields to be best muscular activation as detected from superficial EMG electrodes, and that muscular activation is as better as closer to healthy controls muscular activation patter, particularly in terms of muscular activation timing.

## MATERIALS AND METHODS

## Experimental Set-Up

Patient's overground locomotion has been supported by Ekso (Ekso Bionics, Richmond, CA, USA). Ekso is a wearable bionic suit: it enables individuals with lower limb disabilities and minimal forearm strength to stand, sit and walk over a flat hard surface with a full weight-bearing reciprocal gait under the supervision of a physical therapist. Ekso is intended for nonambulatory and ambulatory post-stroke patients, spinal cord complete, and incomplete injury patients with different etiology, and traumatic brain injury patients. It weighs 23 kg and can be used by individuals who weigh up to 100 kg and range in height from 160 to 190 cm. Patients must have a standing hip width at maximum of 43 cm. Ekso is equipped with four battery-powered motors at the hips and knees: these support or replace deficient neuromuscular function. There are four types of actuation for each patient step: (i) FirstStep, by which a physical therapist actuates steps with a button push; (ii) ActiveStep, by which the patient takes control of actuating steps via buttons on the crutches or walker; (iii) ProStep, by which the patient achieves the next step by moving body weight laterally and then forward; and (iv) ProStep Plus, by which steps are triggered by the user's lateral weight shift. The amount of power contribution to one or both legs during walking can be tuned with three types of assistance for each single step: (i) Bilateral Max Assist, in which Ekso provides full power to both legs and no strength is required from the patient; (ii) Adaptive Assist, in which patients with any amount of lower extremity strength contribute to their walking efforts and Ekso dynamically adjusts to produce a smooth, consistent gait; and (iii) Fixed Assist, where Ekso legs provide a fixed amount of pre-specified power to help patients to complete steps in a predefined amount of time. Within the present study, Ekso has been set with Prostep Plus, and Bilateral Max Assist. Ekso needs to be adjusted to fit patients' anthropometric data for a correct use of the device. In particular, it is necessary to collect hip width, length of right and left upper legs, and length of right and left lower legs.

The muscle activity has been recorded bilaterally with the FREEEMG wireless electromyograph (BTS Bioengineering, Garbagnate Milanese, Milano, Italy). Muscle groups considered for the analysis and placement of the electrodes has been selected accordingly to SENIAM guidelines (Hermens, 1999): tibialis anterior muscle (TA), soleus muscle (SOL), rectus femoris (RF), and semitendinosus muscle (SM). Lower limbs principal muscles have been selected for recording, and in particular, two couples of agonist/antagonist muscles in the proximal and distal compartment respectively, since they are more directly responsible for a correct walking-induced muscles activation profile, and EMG electrodes can be easily positioned without interfering with Ekso.

## Participants