Here, we ask a question that has hitherto not been asked: how "self-like" must a prosthetic hand be in order to be acceptable to the user? The context for the question is as follows: prosthetic hands are expensive and disused for reasons related to lack of embodiment, and cosmetic technology has progressed to where a skilled technologist can make an identical replica of absent anatomy (Altman, 2016), but typically at the

cost of intensive resource investment. Thus, it is incumbent to ask: how exacting must the prosthesis be? We propose a user-preference survey as a platform for testing the need for a prosthetic homologue.

Our protocol builds on previous studies on prosthetic aesthetics, notably lines of inquiry into the uncanny valley (Gee et al., 2005; Cabibihan et al., 2006; Poliakoff et al., 2013), and user preference in cosmetic appeal (Millstein et al., 1986; Carrozza et al., 2005; Kargov et al., 2007; Dalley et al., 2009), and in particular, we believe we put into an empirical framework one of the most-oft recognized design priorities in prosthetic manufacture (Biddiss E. A. and Chau T. T., 2007; Biddiss E. and Chau T., 2007). The protocol presented here in extends naturally on these studies, in doing so, provides an answer to a critical question: given a set of candidate hands which are all equally lifelike, would the user manifest preference for their own hand, and would their preferences be consistent? Whereas these other protocols present hands that of variously life-like or not-so-life-like character—and the degree of lifelikeness is a matter of subjectivity—, our protocol presents only images collected from real humans, which are therefore inherently life-like. This protocol best suits those investigators whose objective is to measure the (putative) importance of prosthetic homology to upper limb prosthetic patients. This protocol is designed to test the following hypotheses: (1) whether individuals show consistent preferences for hand designs, and (2) whether individuals show preferences for hand designs that are homologous to their own hand. Here, we present a full protocol and preliminary results from pilot testing in our laboratory.

### MATERIALS AND EQUIPMENT

### Face-To-Face

Implementation of this protocol in the face-to-face setting will require the following materials:

	- i. An image manipulation software: Adobe Photoshop, GNU Image Manipulation Program (GIMP), Corel PaintShop or similar. The purpose of this software is to create a binarized image of the hand based on the photo, to standardize the image size, and to count the proportion of black pixels ("pixel count").
	- ii. A spreadsheet software: Microsoft Excel, OpenOffice Calc, GoogleDocs Spreadsheet, or similar. The purpose of this software is to create a convenient reference list for sorting images by pixel count.
	- iii. A document or presentation design software: Microsoft PowerPoint, Microsoft Word, or similar. The purpose of this software is to create a series of hand line-ups for the subject to review.

We note that for experienced programmers, this suite of softwares can be obviated by use of a numerical computing environment: Matlab, Octave, R or similar.

	- i. Hand preference data: Most likely a single-page is sufficient; the purpose of this survey is to document which hands were selected by the patient.
	- ii. Demographic and psychological profile: Key variables to collect include age, sex, hand dominance, and history of hand, or arm pathologies. We recommend that some measure of anxiety or body image be used. Particularly well-regarded instruments include the Brief Fear of Negative Evaluation Scale (Leary, 1983), and the Appearance Schemas Inventory: Personal Opinions Questionnaire (Cash and Labarge, 1996). It may also be desirable to ask direct questions about the importance of aesthetics, or design priorities in the hypothetical scenario where the subject needs a prosthetic hand.

### Virtual

Implementation of this protocol in the virtual setting will require the following materials:

	- i. Investigator: Most likely this be a laptop or desktop. Softwares will be needed as described above (Face-to-Face, Item 1).
	- ii. Subject: This can be either a computer or a handheld device, e.g., smartphone or tablet. Most likely, no

additional softwares will be needed. It is important to reduce the software requirements on the subject so as to make the study accessible to a wide participant pool.

We note that both parties will need access to a mutually compatible teleconferencing software, e.g., Skype, Webex, GoToMeeting or similar.

## STEPWISE PROCEDURES

### Face-To-Face

	- i. The subject should see only a chair and the screen.
	- ii. Behind the screen should be the light box, the decoys, and the camera.

blinding; all details will be revealed at the conclusion of the session.

	- i. Ask them which hand is their dominant hand.
	- ii. Place the decoys near to their non-dominant hand and ask them to feel around until they can find the decoys.
	- iii. Investigator should spread the fingers to conform to the template of the hands in the hand bank.

∗Steps ii and iii should be performed simultaneously: Step ii is intended to distract the subject from Step iii.

	- i. Select by pixel count.
	- ii. Select two hands with greater pixel count and two with lesser pixel count.

hand images. Please identify the hand that you find most aesthetically pleasing in each lineup. In particular, we want to know: if you had to receive a prosthetic hand, which hand would you most like your prosthesis to look like?" Note all six choices.

### Virtual

## ANTICIPATED RESULTS

### Image Bank

We have recruited 20 volunteers for our development of our image bank. We exhibit representative samples of these banked images in **Figure 2**. Following our Supplement, each image required ∼90 s to convert from raw image to binarized image with noted pixel count.

### Preliminary Results

We have pilot-tested our Face-to-Face protocol on 32 subjects. Our population comprised healthy volunteers with completely intact anatomy: 13 M/19 F, 26 ± 11 years (range: 19–53 years), all right-hand dominant (with one ambidextrous subject), with an average score on the Brief Fear of Negative Evaluation Score (BFNES) of 33 ± 6 (range: 21–39; range: 0, not at all fearful–60, maximally fearful). Homology scores, i.e., the number of times the subject selected their own hand as the most appealing (max = 6) are shown in **Figure 4**; the mode value was zero selections of one's own hand (**Figure 4A**). Consistency scores, i.e., the number of times the hands selected in lineup pairs were the same (max = 3) were as follows: 1 (n = 16), 2 (n = 10), and 3 (n = 6; **Figure 4B**); all subjects had at least on consistent matching. We note that among those slide-pairs showing consistency within the pair, four subjects (12.5%) selected their own hand in all

three pairs, while 20 subjects with consistent pairings consistently picked a hand that was not their own (**Figure 4C**).

Preliminary hypothesis testing was performed to assess selection of one's own hand, consistency, and the rate of consistent selection of one's own hand, both according to sex, and score on the BFNES, dichotomized about the median. Due to the non-normality of the data, non-parametric tests were used. None of these analyses revealed significant differences between groups (**Figures 5**, **6**). Given that the frequency selection of one's own hand from the hand bank is low, and does not suggest systematic trends by the variables collected in our pilot study, we draw the inference that the prosthetic homologue may not be a particularly important target in custom prosthesis design for all users. However, there will likely be a sub-group of patients for whom a prosthetic homologue is important, though it is not evident that these patients are readily identifiable by sex or BFNES; our pilot data set is insufficiently powered to test for interaction effects.

## NOTES

We make some notes of relevance to the protocol procedures:

1. It is very important that all hand silhouettes used in this study have the same approximate posture; we recommend fingers spread comfortably: sub-maximally, but so that each finger can be seen for its true shape. In our own work, we target angular separation between thumb and little finger of 70◦–90◦ , with middle finger bisecting and parallel to the radius and ulna (**Figure 7**).

FIGURE 5 | Breakout of selection of one's own hand (A), consistency within slide pairs (B) and rates of one's own hand appearing in matched slide pairs (C), breakout by sex.

Hawken, 1996; Papathomas, 2007). Thus, we re-emphasize the importance of scaling images appropriately so that it is the shape of the hand, and not the imprint or size of the hand that drives the subject's selection.

4. Continuing on Note #3, we designated pixel count as the single parameter best poised to match the participant's hand to the hand bank, based on two justifications: physiological, and practical. As described above, the amount of black versus white content in a binarized image will have substantial impact on the viewer's attention; given this, we wanted to avoid biasing participants to picking hands with extremely high pixel counts: rather, we concluded that by selecting images with similar pixel counts, we could eliminate this source of variance, increasing the likelihood of detecting preferences associated with hand shape. Regarding pragmatics, we could have extracted features from the images, e.g., based on hand contour or finger spread, etc., but discarded these designs on the basis that their complexity would inhibit adoption of this study by a wide range of researchers; by selecting pixel count—a feature that is easily extracted from any image without sophisticated image processing steps—we hope to make this protocol more accessible among the diverse community of investigators with interest in aesthetics and prosthetic design.

the Virtual experiment truly compromises the subject naïveté remains to be tested, but must be recognized as a possible source of bias. Creative use of decoys will most likely be helpful here.

7. This hand bank is prepared prior to opening the study to enrollment so that there are an ample set of candidate hands to present to first participant. We propose soliciting volunteers to allow their hands to be photographed, and to binarize these images, saving them in a folder for accessing during the data collection phase. We suggest that this pre-collection phase of hand bank creation will give the study critical opportunity to refine their image binarization technique. Over time, our team was able to reduce the time required to binarize an image from 5 min to <30 s; this is a valuable time savings in terms of maintaining participant focus. The reduction in time burden is attributable primarily to improved acquaintance with the routine for binarization; familiarity greatly improves efficiency. We refer the reader to our Supplementalary Materials for step-by-step procedures for image binarization, and recommend MS PowerPoint or equivalent for creating the lineups.

Regarding the implications of this protocol: we firstly recognize the limitations of using a 2-dimensional test (selection of images) for an inherently 3-D problem, i.e., embodiment of a prosthetic device. However, we believe that is a useful first line of inquiry into the necessity and sufficiency of a design criterion based on homology between the prosthetic device and the anatomy that it replaces, and that this is an attractive paradigm than the alternative: 3-D printing a hand for each subject.

It is prudent to discuss two terminologies used here: embodiment, and the homologue. Embodiment is generally defined as the integration of an artificial limb into one's own body schema, i.e., the fusion of body and perception (Mulvey et al., 2009). This concept is often invoked in the context of someone asked whether they are able to "make it feel like me." While our protocol does not utilize actual prostheses, we believe that our prompt to the participants ("Which hand would you most like your prosthesis to look like") brings our study comfortably into the realm of embodiment. Further, in this work, we are coining the term prosthetic homologue, which has heretofore not been described in any scientific literature known to the authors or their collaborators. Our use of the term is meant to pair the concept of similarity (to one's own anatomy; the "homologue") to anatomical replacement (the prosthesis). We note the concept of homology is occasionally referred to in the literature related to the uncanny valley (Chaminade, 2006; Tondu, 2012; Kaerlein, 2015), but is used in a way that connotes similarity to human anthropomorphism, and not per se any one person's anatomy (or one's own anatomy). In many settings, the prosthetic homologue cannot directly be assessed: a limb-deficient individual has no anatomy from which to draw the comparison; the homologue in this case would be an abstract construct, that can only be approached conceptually, but not tested. However, in healthy persons, while there is no direct opportunity for prosthetic replacement, the anatomy is present, can be measured, and preferences in the hypothetical can be tested.

By presenting pilot data as a preliminary result, we intend to provide investigators with basis for formulating study designs, i.e., requisite sample sizes for pre-specified parameters related to statistical power. This entire protocol can be executed via freewares, i.e., at no expense beyond the base materials; given the ubiquity of digital cameras and web access, we believe that this is accessible protocol that can be readily implemented in a wide variety of settings. Lastly, we note that all data collection activities described in this manuscript were performed under the authorization of the University of Hartford Institutional Review Board, following provision of written informed consent.

### ETHICS STATEMENT

University of Hartford Human Subjects Committee (FWA00003578). All participants signed an informed consent prior to participation in this study.

### AUTHOR CONTRIBUTIONS

MW: Lead investigator, primary study architect. All authors participated in the design of the study, drafting and approval of the manuscript, and take responsibility for its contents.

### ACKNOWLEDGMENTS

The authors gratefully acknowledge the work of Amber Sayer, Steve Sousa, and Chris Welch.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fnbot. 2016.00021/full#supplementary-material

### REFERENCES

and L. Fortunati (Cham: Springer International Publishing), 77–88. doi: 10.1007/978-3-319-15672-9\_7

**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.

The reviewer EC and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.

Copyright © 2016 Dornfeld, Swanston, Cassella, Beasley, Green, Moshayev and Wininger. 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.

*Alix Chadwell, Laurence Kenney\*, Sibylle Thies, Adam Galpin and John Head*

*Centre for Health Sciences Research, University of Salford, Salford, UK*

Users of myoelectric prostheses can often find them difficult to control. This can lead to passive-use of the device or total rejection, which can have detrimental effects on the contralateral limb due to overuse. Current clinically available prostheses are "open loop" systems, and although considerable effort has been focused on developing biofeedback to "close the loop," there is evidence from laboratory-based studies that other factors, notably improving predictability of response, may be as, if not more, important. Interestingly, despite a large volume of research aimed at improving myoelectric prostheses, it is not currently known which aspect of clinically available systems has the greatest impact on overall functionality and everyday usage. A protocol has, therefore, been designed to assess electromyographic (EMG) skill of the user and predictability of the prosthesis response as significant parts of the control chain, and to relate these to functionality and everyday usage. Here, we present the protocol and results from early pilot work. A set of experiments has been developed. First, to characterize user skill in generating the required level of EMG signal, as well as the speed with which users are able to make the decision to activate the appropriate muscles. Second, to measure unpredictability introduced at the skin–electrode interface, in order to understand the effects of the socket-mounted electrode fit under different loads on the variability of time taken for the prosthetic hand to respond. To evaluate prosthesis user functionality, four different outcome measures are assessed. Using a simple upper limb functional task prosthesis users are assessed for (1) success of task completion, (2) task duration, (3) quality of movement, and (4) gaze behavior. To evaluate everyday usage away from the clinic, the symmetricity of their real-world arm use is assessed using activity monitoring. These methods will later be used to assess a prosthesis user cohort to establish the relative contribution of each control factor to the individual measures of functionality and everyday usage (using multiple regression models). The results will support future researchers, designers, and clinicians in concentrating their efforts on the area that will have the greatest impact on improving prosthesis use.

#### *Edited by:*

*Claudio Castellini, German Aerospace Center, Germany*

#### *Reviewed by:*

*Dennis J. McFarland, Wadsworth Center, USA James Wright, Western Sydney University, Australia*

> *\*Correspondence: Laurence Kenney<EMAIL_ADDRESS>
*Received: 13 June 2016 Accepted: 25 July 2016 Published: 22 August 2016*

#### *Citation:*

*Chadwell A, Kenney L, Thies S, Galpin A and Head J (2016) The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control. Front. Neurorobot. 10:7. doi: 10.3389/fnbot.2016.00007*

Keywords: prosthesis, myoelectric, activity monitoring, control, upper limb, functionality assessment

## 1. INTRODUCTION

Statistics relating to the prevalence of limb absence and provision of prostheses are poor. However, data from the United States in 2005 show that ~41,000 people were living there with major upper limb absence (Ziegler-Graham et al., 2008), which equates to 1 in 10,000 people. Furthermore, each year in England ~5–6,000 major limb amputations are carried out (NHS Choices, 2014), of which approximately a fifth are undertaken on the upper limb (Lusardi et al., 2013; NHS Scotland, 2014) and most commonly at the trans-radial (forearm) level (UNIPOD – United National Institute for Prothetics and Orthotics Development, 2010/11). In addition, congenital deformities contribute significantly to the number of people living with upper limb absence, although data on prevalence are somewhat inconsistent (Kyberd et al., 1997; UNIPOD – United National Institute for Prothetics and Orthotics Development, 2010/11; Head, 2014).

Three types of upper limb prostheses are available to people with limb absence: cosmetic prostheses, which are primarily designed to restore appearance and symmetry; and functional prostheses, which are either body-powered via the use of a harness and cables, or electrically powered via rechargeable batteries. Electrically powered prostheses, commonly known as myoelectric prostheses, are controlled by electromyographic (EMG) signals created in the residual musculature, which are picked up by electrodes housed within the prosthetic socket. However, despite the potential offered by myoelectric hands, prosthesis users report these devices to be challenging to control (Biddiss and Chau, 2007a; Peerdeman et al., 2011; Head, 2014; Engdahl et al., 2015) and to still be limited in function (Biddiss and Chau, 2007a; Peerdeman et al., 2011). These user reports are supported by the results of clinical assessment tests in which upper limb prosthesis users generally perform at less than 50% of the level of their anatomically intact counterparts (Mathiowetz et al., 1985; Grice et al., 2003; Farrell et al., 2005; Kyberd et al., 2009; Metzger et al., 2010; Bouwsema et al., 2014; Sobuh et al., 2014). Unsurprisingly, passive-use and rejection of myoelectric prostheses have been reported as problems (Biddiss and Chau, 2007b), leading to over-use injuries of the intact limb (Jones and Davidson, 1999; Gambrell, 2008; Østlie et al., 2011).

In response to user feedback, attempts have been made to improve the control of myoelectric prostheses. Since current clinically available devices are "open loop" with respect to the user, promoting reliance on visual feedback, recent advances have frequently focused on providing users with tactile feedback (Antfolk et al., 2013; Kim et al., 2014; Tee et al., 2015; Oddo et al., 2016; Xu et al., 2016). However, Saunders and Vijayakumar (2011) demonstrated that, although the introduction of feedback can improve control of myoelectric prostheses, other characteristics of the prosthesis may be equally, or even more important in determining the ability of the user to control their prosthesis. In their study, participants demonstrated that when using a "perfect" fast-responding prosthesis they were able to demonstrate good levels of control over grip force even in the absence of any feedback; however, in the presence of "*uncertainty*" as to how the hand would react (presented in the form of random delays in prosthesis response time), their control of the prosthetic hand decreased. Saunders concluded that if the central nervous system (CNS) is able to produce accurate predictions of anticipated prosthesis behavior (forward models), then reliance on feedback from the hand is reduced. Saunders and Vijayakumar (2011) also noted that a degree of "*uncertainty*" was an inherent part of myoelectric prosthesis use. This observation was further investigated by Head (2014) who identified that the standard method for housing electrodes in prosthetic sockets can result in EMG signal artifacts, or loss of electrode contact with the skin, leading to "*unpredictability*" in the response of the prosthesis to muscle contractions. Finally, despite recent findings in anatomically intact subjects (Terlaak et al., 2015) challenging the assumption, there is a widely held belief that there is a relationship between the level of skill in producing the required EMG signals and prosthesis control.

In summary, despite technological advances, control of myoelectric prostheses remains challenging, leading to device rejection and associated over-use injuries of the intact limb. Introducing feedback into the system is one possible solution to enhance prosthesis control for improved "*functionality*" and "*everyday usage*," however, research into the different aspects of the prosthesis control chain (e.g., "*unpredictability*" in the system and "*EMG skill*" of the user) may be equally important. Here, we introduce a novel protocol, including purpose-built, portable instrumentation that has been designed for the assessment of these individual factors contributing to feed-forward prosthesis control in relation to aspects of overall upper limb performance. Specifically, the protocol assesses how well a myoelectric user can control their EMG signals ("*EMG skill*") and how reliably the electrodes pick up the signals ("*unpredictability*"). These outcomes are then related to measures reflecting how close the kinematic and gaze patterns of the user are to healthy norms ("*functionality*") during performance of a structured multistage manual task, and how often the myoelectric prosthesis is used in everyday life ("*everyday usage*"). It is important to note the separation of these two performance measures. Literature has shown that an increase in upper limb "*functionality*" as assessed using clinical tests may not necessarily correspond to an equivalent improvement in everyday arm use (Bailey et al., 2015). By comparing the control factors against "*functionality*" and "*usage*," it should be possible to identify which factor(s) has/have the greatest impact on overall performance. Longer-term, researchers, designers, and clinicians can then ensure that their efforts are concentrated on the area(s) that will be of greatest benefit to prosthesis users.

In this paper, we introduce the experimental procedures to characterize key factors contributing to the feed-forward prosthesis control chain, namely skill in controlling the EMG signals ("*EMG skill*") and "*unpredictability*" in transduction of the EMG signal (between the skin and the electrode). We also describe the measures designed to capture the user's overall upper limb performance ("*functionality*" and "*usage*"). Initial results of our pilot work and their discussion are included to demonstrate the feasibility of the protocol. Furthermore, we propose a data analysis method to be used in the main study, which attributes variance in measures of performance to one or more elements of the control chain.

As the protocol is complex and involves the description of several experimental setups, we have kept the detail in the main body of the paper to a minimum and make use, where appropriate, of Supplementary Material.

### 2. METHODS AND ANALYSIS

### 2.1. EMG Skill

The muscle groups used to control the opening and closing of myoelectric hands and their associated neural pathways differ from those used in the anatomical hand (Bongers et al., 2012). It is, therefore, reasonable to assume that opening/closing the hand with this "new" set of muscles in response to a relevant prompt may be less intuitive and require an increase in mental processing time as reflected in an increased response time (Kuiken et al., 2004, 2007). It is also reasonable to assume that practice using this "new" set of muscles to open/close the hand may decrease the response time (Ando et al., 2002; Radhakrishnan et al., 2008).

To establish the mental processing time to activate the "new" set of muscles, the subtractive method developed in the 1860s by Donders (1869) can be used. Donders proposed the use of different types of reaction time tests to establish the time spent undertaking cognitive and motor processes. Such reaction time tests include the simple reaction time (SRT) test in which participants are aware of the required response before stimulus presentation, and the choice reaction time (CRT) test where the stimulus dictates the required response. Donders segmented the response time into the time taken to perceive the stimulus (signal perception), time taken to decide how to respond (decision time), and time taken to activate the neurons (motor response) (**Figure 1**). For the SRT test, there is no decision time as the required response is already known and the person is primed to react. Donders also declared that the time for signal perception and motor response does not vary between the tasks. Consequently, he suggested that subtracting the SRT from the CRT provides information as to the decision time to undertake the CRT test, or in this case, how long it takes the person to decide which muscles to activate to operate the prosthesis ("*Decision Time*"). Accordingly, this study uses reaction times measured under two different conditions to characterize the "*Decision Time*," and associated tests are termed "*Reaction Time Tests*" (see section 2.1.2).

Furthermore, the ability to control the amplitude of the EMG signal using the musculature of the residual limb can be measured through the performance of a series of continuous signal tracking tasks. There are two main types of tracking tasks: static and dynamic. For a static tracking task the subject is required to match their EMG signal to a target amplitude (Alcaide-Aguirre et al., 2013), while a dynamic tracking task involves modulating the amplitude of the EMG signal to match a moving target (Guo et al., 2009; Corbett et al., 2011; Alcaide-Aguirre et al., 2013; Lobo-Prat et al., 2014). Most clinically prescribed myoelectric prostheses are equipped with proportional control, meaning that it is not only important that a user is able to generate a signal strong enough to activate the hand but that they can also modulate the level of the signal to allow for control of the hand speed and the grip force. Dynamic tracking tasks take different forms: some contain a repetitive signal modulation, such as a sinusoidal wave of a set amplitude (Guo et al., 2009), while others vary the amplitude at random (Corbett et al., 2011; Lobo-Prat et al., 2014). For this study, we use a commercially available software package originally designed for the clinical training of myoelectric prosthesis users, which provides us with a means to test user performance in tracking both static and random amplitude modulated targets, using their EMG signal. The approach also allows us to use clinical EMG electrodes (rather than laboratory-standard EMG gel electrodes), thereby reflecting the transduction, signal processing, and amplification used in practice. We term the set of static and dynamic tracking tasks to be "*Tracking Tasks*" (Section 2.1.3).

Details of the number of repeats for each test are provided in **Table 1A**.

#### 2.1.1. Electrode Placement

The "*EMG skill*" analysis tests require an "*ideal*" electrode placement on the residual limb to ensure that the participant is able to perform to their best ability. This "*ideal*" placement requires the electrode to be placed in the optimal location, with the optimal gain and good contact with the skin.

Slight variations exist in the methods used to find the optimal location for the electrodes; for this protocol, we use the methods taught to student prosthetists at the University of Salford. Rather than use the participant's own electrodes, which would necessitate dismantling the prosthetic socket, we use one of two standard electrodes, selected to best match the participant's own type of electrode (Ottobock 13E200 = 50 or RSL Steeper SEA200). Optimal settings for the selected electrode are found using the clinical assessment tool Myoboy® (Ottobock Gmbh). Initially, the gain for each electrode is set at a mid-level of 3–4. Participants are then asked to repeatedly and consistently contract the muscle to a comfortable level. The electrode is initially placed in the center of the muscle bulk and the signal level is noted. The electrode is then moved in each of four directions (up, down, left, and right) from the starting location, by half an electrodes width. If the amplitude

#### TABLE 1A | Protocol summary – tests for the assessment of "*EMG skill*".

#### TABLE 1B | Protocol summary – tests for the assessment of "*Unpredictability*".

#### TABLE 1C | Protocol summary – tests for the assessment of "*Functionality*" and "*Everyday Usage*".

of the signal is greater in any of these new locations, the process is repeated using the new location as the starting point. This is continued until the position with maximum signal amplitude is found, and the location marked using an indelible pencil.

The gain settings are adjusted until the participant is able to comfortably achieve a post-processed signal amplitude, recorded by Myoboy®, between 30 and 60 and separation between the two signals greater than 5.1 To achieve consistent good contact of the electrodes with the skin, they are bandaged in place using elasticated bandages. The difference between the optimal location and gains, and the location and gains for the participant's own prosthesis, is noted.

#### 2.1.2. Reaction Time Tests

For these tests, the "*ideal*" electrode placement (Section 2.1.1) is used to control a MyoHand VariPlus Speed (Ottobock Gmbh).

A schematic of the experimental setup is shown in **Figure 2A**. The participant begins each trial with the prosthetic hand in a neutral position. In front of the participant is a custom-made

<sup>1</sup>The manufacturers do not disclose the details of the scale used to represent signal amplitude, hence, the units are not reported.

reaction time box with two 10-mm red LEDs serving as stimuli for hand opening (top) and closing (bottom), and one 5-mm red LED in their middle to focus the subject's attention at the start of each trial. The anatomical hand is placed on a large blue button situated on the bottom portion of the box. The trial begins when the participant indicates that they are ready by pressing the blue button. Each trial then starts with the 5-mm LED illuminating for 1 s to attract the participant's attention. At a randomly generated time between 2.5 and 3 s (Poliakoff et al., 2013) after the subject pushes the button, one of the 10-mm LEDs will then illuminate for 1 s. Once the 10-mm LED turns on, the participant should then either open (if top LED) or close (if bottom LED) their prosthetic hand in response. For the SRT Test, the subject is aware which LED will illuminate, i.e., which response is required. For the CRT Test, the subject needs to respond with either hand opening or closing, dependent on whether the top or bottom LED is illuminated. For all reaction time tests, an electronic goniometer (Biometrics Ltd) is attached across the proximal knuckle of the index finger to measure the movement of the prosthetic hand, thereby allowing for identification of the onset of hand opening or closing.

Details of the instrumentation used in SRT and CRT trials are shown in **Figure 2B**. The reaction time box and goniometer are controlled via Arduino Leonardo boards (www.arduino.cc) communicating over serial with Matlab (The Mathworks Inc.). The wait time and LED number are sent from Matlab to Arduino1 to start the test. Arduino1 waits for acknowledgment that the user is ready, based on their button press. Arduino1 then initiates recording of the goniometer data on Arduino2 and controls the LEDs on the reaction time box. Matlab then analyzes the goniometer data establishing the reaction time, which is sent back to Arduino1 and displayed to the participant. A T9545 goniometer adaptor (Thought Technology Ltd.) and TT Sensor Isolator ST9405AM (Thought Technology Ltd.) are used to interface between the goniometer and Arduino2.

#### 2.1.3. Tracking Tasks

This test uses commercially available assessment tools from Ottobock Gmbh that are routinely used in clinical care. The Myoboy® hardware is designed to measure the signal from the clinical electrodes and send it to a computer. Using the PAULA (Prosthetist's Assistant for Upper Limb Architecture) software, the signal can be viewed and the user can then undertake activities to train and improve signal control. The "*ideal*" electrode placement (Section 2.1.1) is used with the electrodes connected to the Myoboy® hardware. Two different aspects of the PAULA software are used, one for the "*static tracking task*" and one for the "*dynamic tracking task*."

The "*static tracking task*" uses the myo-testing signal visualization screen (**Figure 3A**). The boundary lines within this screen are adjustable and in this protocol are set to 39 and 51; these values were determined through pilot work as a level that is sufficiently challenging for the more skilled participant, yet somewhat achievable for the least able. The participant is given three contraction attempts to keep their signal amplitude within the boundaries for each muscle. Each contraction is 3 s long from the moment the signal first crosses the lower boundary line. Participants are scored on the percentage of time the signal remains within the boundaries.

The "*dynamic tracking task*," on the other hand, uses the training "car game" within PAULA. The task involves steering a car through gaps in approaching walls that fluctuate in height (**Figure 3B**). The game level is set in the middle of the available options at 5, and the training time is 1 min which during pilot work proved to be long enough that no one achieved a perfect score, without being too long that people who were struggling stopped trying. The height of the car is controlled using the EMG signal; muscle contraction elevates the car on the screen and muscle relaxation drops the car to the bottom of the screen. Beginning with the hand-open signal the participant must steer the car through the approaching gaps that cycle between being high and low (contraction and relaxation). Participants are given two attempts to get the best score they can achieve, defined as the percentage of gaps successfully passed through without crashing (Part 1a). The task is then repeated for the hand-close signal (Part 1b). Finally, the participant must control two cars at once using both signals (Part 2). During part 2, the cars are set up so that when one muscle is contracted the other one should be relaxed, assessing the ability of the participant to separate their signals, while cycling between hand opening and closing.

### 2.2. Effects of Electrode Interface Condition on EMG Signal Transduction

Good electrode contact with the skin is required for reliable transduction of the EMG signal. Prosthesis electrodes (known as myoelectrodes) are "dry" metal electrodes housed in a plastic case; a small gap in the prosthesis socket is designed to house the myoelectrode; two rubber projections extend from each end of the casing, which locate within pre-manufactured slots in the socket walls. Although a surprisingly neglected area, it is established that the design of prosthetic sockets and associated electrode housings can lead to problems in the transduction of the EMG signal. For example, applied load may cause the socket to move relative to the residual limb and, hence, produce signal artifacts, or electrode contact may be lost altogether (Head, 2014). Furthermore, it is possible that re-donning of the socket may lead to the electrodes moving from the optimal location (see Electrode Placement), leading to crosstalk from other muscles. These factors constitute "*unpredictability*" in the transduction of the EMG signal, leading to "*uncertainty*" as to the response of the prosthetic hand to neural commands.

Our protocol builds on previous work in this area (Head, 2014) to assess two key aspects of "*uncertainty*": (1) whether the hand responds when the user desires it and (2) whether the hand activates unexpectedly. Specifically, to assess the impact of the socket-housed electrode fit on these two "*uncertainty*" measures, participants complete a set of tasks with the forearm held at two different angles, under three electrode interface conditions (**Figure 4**): (1) "*Ideal*" – the electrodes are placed in the optimal position on the residual limb and held in place using elastic bandage as in Section "2.1.1" (**Figure 4A**). The electrodes are connected to the MyoHand VariPlus Speed (Ottobock Gmbh) as in Section "2.1.2," which is sat on the table top; using this method, there should be minimal or no movement of the electrodes in relation to the skin. (2) "*Normal*" – the prosthesis is worn as normal, and the electrodes are housed in the prosthetic socket (**Figure 4B**). From this part of the study onward, the participant uses their own prosthesis with the electrode location and gain settings which they would use in everyday life. (3) "*Additional load*" – the prosthesis is worn as normal; however, an additional 500 g load is strapped to the hand to simulate the weight of an object, such as a full jar (**Figure 4C**).

Tasks are undertaken with the arm in postures that are representative of those encountered during daily activities, such as reaching to a shelf, or down into a drawer, corresponding to ~45° above and below the horizontal. Forearm angles from the horizontal are measured using an inertial measurement unit (IMU). For this study, an Xsens MTw sensor (Xsens Technologies B.V.) is used. The IMU is placed on the back of the wrist just proximal

to the ulnar styloid. The *x*-axis is aligned along the forearm axis pointing toward the hand. For our pilot work, a proprietary algorithm was used, which outputs orientation components based on Euler angles (XYZ earth fixed); however, in the longer-term, this will be replaced with an algorithm that calculates the orientation of the *x*-axis relative to gravity (Sun et al., in press).

The set of tasks performed at each of the two arm orientations, for each of the three electrode interface conditions are described in the following two sections.

#### 2.2.1. Reaction Time Tests

The impact of the electrode interface conditions on variability in reaction times is assessed using the equipment described in 2.1.2 above. Participants begin with the "*ideal*" electrode interface; the simple reaction time (SRT) test is undertaken at each of the two arm postures. The test is then repeated for the other two interface conditions ("*Normal*," "*Additional Load*") at each of the two arm postures. The number of repeats is detailed in **Table 1B**. The spread in reaction times is compared across the electrode interface conditions, between the "*ideal*" interface and the two socket-housed electrode conditions ("*Normal*," "*Additional Load*").

#### 2.2.2. Transitioning between Arm Postures

Transitions from one posture to another may, in the case of a poor fitting socket, cause an EMG artifact and, hence, cause the

prosthetic hand to open or close when the user does not desire it (Head, 2014). Such an event could lead to the user dropping or squashing an object. Therefore, between each set of "*reaction time tests*" (see Section 2.2.1), prosthetic hand posture is recorded as the arm moves between the two arm postures. The hand begins each transition either completely open or completely closed, and prosthetic hand posture is recorded throughout the transition using the goniometer (see Section 2.1.2); any undesired activation, i.e., opening or closing of the hand is recorded.

### 2.3. Functionality Assessment

Upper limb prosthesis user functionality is typically appraised using an appropriate, validated assessment tool, such as the Southampton Hand Assessment Procedure (SHAP) (Light et al., 2002). In common with a number of other clinical tests, functionality is evaluated on the time taken to successfully complete specific tasks. Faster completion times are assumed to correspond to higher levels of functionality. Although the duration of task performance is one measure of functionality, it provides no information on how tasks are completed. A number of studies have shown that combining several outcome measures provides a more complete picture of the functional abilities of prosthesis users (Hill et al., 2009; Wright, 2009; Lindner et al., 2010; Bouwsema et al., 2012). Kinematic outcome measures and gaze behavior can be recorded during the performance of multistage tasks to provide information that complements speed of performance measures. It has previously been shown that performance characterized using these measures clearly differentiates amputees from anatomically intact controls (Bouwsema et al., 2012; Sobuh et al., 2014).

When faced with a novel task, young children are known to try a number of different movement trajectories, allowing the CNS to build a representation of the optimum trajectory (Schneiberg et al., 2002). When faced with structured multistage manual upper limb tasks, novice prosthesis users have been shown to demonstrate similar trends (Major et al., 2014). During the first few task attempts, variability in the linear acceleration patterns of the forearm is high; however, after practice with the prosthesis, variability has been shown to decrease (Sobuh, 2012). Moreover, Bouwsema et al. (2010) demonstrated that prosthesis users demonstrate a later onset of hand opening during reach-to-grasp movements than anatomically intact subjects, and a plateau in the hand aperture between opening and closing around the object.

Furthermore, previous studies undertaken by Bouwsema et al. (2012) and Sobuh et al. (2014) have shown that the gaze behavior of inexperienced prosthesis users differs from that of anatomically intact controls, however, with practice gaze patterns approach those of controls. A more functional user would be expected to demonstrate a larger number of "look-ahead-fixations" and spend less time concentrating on the prosthetic hand. In a multistage task, "look-ahead-fixations" involve gaze fixation on an area of the task critical to a future task component (such as looking at the object to be grasped, or the location it will be moved to while completing the reach, rather than concentrating on the hand). Fewer transitions between areas of interest (AOIs, e.g., hand, grasp area of the target) would also be expected. Interestingly, participants who self-report rarely using their devices in everyday life have been shown to demonstrate more gaze transitions, irrespective of their functional ability with the device (Bouwsema et al., 2012). Prosthesis users are reliant on visual feedback, as such it would be expected that patterns in gaze behavior may be related to a person's knowledge as to how their hand will respond. If a participant cannot accurately predict the response of their prosthesis, it is possible that this will be reflected in the number of gaze transitions.

We, therefore, assess participants' performance with their prosthesis using a structured multistage manual task, which involves the reaching for, grasping, then placing and releasing of a cylinder in a tube. Three levels of task difficulty are available to the participants (as described below). Performance is then characterized based on number of successfully completed trials and task difficulty, time to complete the task, aperture onset delay, plateau time during reach-to-grasp, kinematic variability in movements (using accelerometry) and gaze behavior over successive trials.

#### 2.3.1. Task Design

Previous work has suggested that certain movements are prone to cause users with poor fitting sockets particular difficulties in prosthesis control, possibly as a result of artifacts caused by electrode movement in relation to the skin or separation from the skin (Head, 2014). These include movements that would be achieved through "pronation" or "supination" in anatomically intact participants. A set of three multistage unilateral tasks ("*cylinder tasks*") have been developed (termed "*tasks A–C*"), the harder of which ("*tasks B and C*") encompass these movements and, hence, present a significant challenge to some participants. Each participant attempts 10 trials (**Table 1C**) of 2 of the 3 tasks, as follows. All participants begin with the medium difficulty level ("*task B*"). Using the prosthesis, participants reach to grasp a cylinder (dia. 52 mm, length 200 mm, weight ~350 g), lift and rotate it through 90° to the horizontal, place it into a horizontally orientated tube (inner dia. 64 mm, length 100 mm), and then release it returning their hand to the starting position. Participants who have a prosthesis with a wrist rotator are asked not to use this function during completion of any of the manual tasks. If the participant is successful in completing over 80% of the trials without dropping the cylinder, they move to "*task C*" in which the tolerance between the cylinder (dia. 52 mm, length 200 mm, weight ~350 g) and the tube (inner dia. 58 mm, length 100 mm) is reduced. If they are unsuccessful in completing 80% of the medium difficulty trials ("*task B*") they perform the easier task ("*task A*"), in which the cylinder is placed vertically into a vertically orientated target tube with the same dimensions as "*task B*" (inner dia. 64 mm, length 100 mm).

As before, participants wear sensors allowing kinematics to be assessed and an eye tracker to record gaze behavior. IMUs (Xsens MTw) are worn on the wrist of the prosthesis and on the chest,2 an electronic Goniometer (Biometrics Ltd.) is worn across the proximal knuckle of the index finger, and participants wear a Dikablis Professional Eye Tracker system (Ergoneers). All data are sampled at 50 Hz. The three systems are synchronized using an arcade style button.

#### 2.3.2. Performance Evaluation

To score performance, the task is split into five movement components and the participants gain points for each section they complete successfully. Points are gained for successful completion first time of (1) reach-to-grasp, (2) lifting and rotating the cylinder, (3) placing the cylinder all the way into the tube, (4) releasing the cylinder, and (5) returning to the start position. Half points are allocated if the movement component was completed after a second attempt (provided the cylinder was not dropped or knocked over). Additional points are added to the total to reflect the level of difficulty the subject was able to perform the task at (0, easy task "*A*"; 1, medium task "*B*"; 2, difficult task "*C*"). The participant starts and ends the movement with their hand on an arcade style button from which timestamps are generated, allowing for task duration to be calculated.

#### 2.3.3. Quality of Movement

Quality of movement encompasses both the pattern of hand aperture during reach and the movement variability throughout the task. It is possible to determine the end of the reach phase by analyzing the goniometer data. When the task begins, the hand is completely closed; the hand then opens, before closing again around the cylinder to generate a "*transport plateau*" as the object is transported. It has been shown that prosthesis users demonstrate a delay in opening the hand at the start of reach, demonstrated by an "*onset plateau*," and a delay between opening and closing the hand, termed the "*reach plateau*" (Bouwsema et al., 2010). The start of the "*transport plateau*" is taken as the end of the "reach-to-grasp" component of the task. By segmenting the "reach-to-grasp" component of the task, the delay in onset of hand movement (the length of the "*onset plateau*") is calculated as a percentage of the "reach-to-grasp," and the length of the "*reach plateau*" is calculated as a percentage of the "reach-to-grasp." Furthermore, using the wrist-mounted IMU, the kinematic variability in the linear acceleration of the forearm between tasks is assessed using the methods developed by Thies et al. (2009).

#### 2.3.4. Gaze Behavior

For the purposes of analyzing the eye tracking videos, the task area is split into six AOIs: (1) start point (button), (2) prosthetic hand, (3) "grasp critical" area (GCA) (bottom half of the cylinder for "*tasks A and B*," top half for "*task C*"), (4) other "location critical" half of the cylinder (LCA) that is required to be placed into the tube, (5) tube, and (6) LED. The percentage of time spent looking at each AOI is calculated, alongside the number of times that the gaze location transitions between each of these areas. Finally, the percentage of time spent looking at areas of the task relevant to subsequent components of the task ("look-ahead-fixations") is calculated for each point in the task (e.g., the cylinder and tube during "reach-to-grasp," or the tube during manipulation and transport).

### 2.4. Everyday Usage

Current methods of quantifying everyday prosthesis use involve self-report (Roeschlein and Domholdt, 1989; Sherman, 1999; Gallagher and MacLachlan, 2000; Raichle et al., 2008), which is known to be prone to recall and bias errors (Metcalf et al., 2007; Brown and Werner, 2008). Accelerometer-based activity monitoring (Noorkõiv et al., 2014) provides an opportunity to observe actual prosthesis use outside of the clinical environment; however, to date no studies have been published on a cohort of upper limb prosthesis users. We have adapted a protocol developed for stroke patients (Bailey et al., 2015). This research

<sup>2</sup>The trunk sensor is used for setting up the cylinder tasks; the distance of the cylinder from the resting hand position should allow the participant to reach the cylinder without leaning forwards. The trunk sensor will also record trunk compensatory movements during task performance.

involved participants wearing an activity monitor (Actigraph GT3X+) on each of their wrists while they went about their normal daily activities. The Actigraph monitors provide continuous logging of raw accelerometer data (sampled at 30 Hz). The data are downloaded using proprietary software, filtered and down sampled to 1 Hz. The processed data are expressed as activity counts (0.001664 g/count) (Actigraph Corp, 2015), which are converted into vector magnitudes (sum of the counts along each axis *x y z* 2 2 <sup>2</sup> + + ). For each second of data, Bailey et al. (2015) combined the vector magnitudes from each of the two wrist worn monitors (dominant and non-dominant arm) to inform on the magnitude of activity across both arms, expressed as the "*bilateral magnitude*" (VMDom + VMNonDom), and the contribution of each arm to the activity, expressed as the "*magnitude ratio*" [ln(VMNonDom + VMDom)].

Bailey found that in healthy, anatomically intact controls, the median "*magnitude ratio*" was around zero (symmetrical bilateral arm use); however, in the stroke cohort, the "*magnitude ratio*" was skewed toward unilateral Non-Paretic (unaffected) arm use. In general, participants in the stroke cohort who demonstrated higher levels of functionality [according to the Action Research Arm Test (McDonnell, 2008)] also demonstrated "*magnitude ratios*" closer to those of the healthy control subjects; nevertheless, a third of participants demonstrated a median "*magnitude ratio*" representing unilateral non-paretic arm use, regardless of their functionality with the paretic arm.

For our study, the activity monitors are placed on the anatomical wrist and the wrist of the myoelectric prosthesis. The monitor is not transferred to other prostheses the participant may wear (e.g., body-powered), as only the times when the myoelectric prosthesis is in use are of interest to this study. Participants are invited to wear the monitors for 1 week.

## 2.5. Pilot Study

### 2.5.1. Recruitment

The purpose of this pilot study was to assess the robustness and feasibility of the protocol before undertaking the main study with a cohort of myoelectric prosthesis users. Ethical approval was granted by the University of Salford School of Health Sciences Research Ethics committee (*REF: HSCR 15-130*) to pilot the above protocol with anatomically intact subjects using a prosthesis simulator designed to fit over their intact arm (**Figure 5**), and myoelectric prosthesis users recruited from the University of Salford Prosthetics and Orthotics Professional Patient Database. Inclusion criteria for the latter were (1) an amputation or congenital limb loss at the trans-radial level, (2) owning a myoelectric prosthesis, and (3) over 18 years of age. Exclusion criteria were (1) bilateral limb loss, (2) injury to the residual limb at the time of testing, and (3) using single site muscle control.

#### 2.5.2. Data Analysis

### *<IP_ADDRESS>. Factors Affecting Prosthesis Control*

As described above (see Sections 2.1 and 2.2), "*EMG skill*" and "*uncertainty*" both affect control of the prosthesis. Multiple variables are generated as part of this protocol that characterize these factors, and which must be combined into overall scores for skill in controlling the EMG signals ("*EMG Skill Score*"), and "*uncertainty*" introduced by the electrode interface ("*Uncertainty Score*"). For this reason, the pilot study data were reduced to ordinal data.

Specifically, the "*EMG Skill Score*" is devised of the reaction time difference between the choice and simple reaction times (termed "*Decision Time*," see Section 2.1), and the scores from the "*Tracking Tasks*." To ensure that the reaction times reported were not biased by early or late reactions, any responses faster than 100 ms or slower than 1000 ms were excluded from the analysis (Press et al., 2005). When combining the three scores contributing toward "*EMG Skill*," the "*Dynamic Tracking Task*" score was given a higher weighting, since accurately proportionally controlling a dynamic and noisy signal poses a greater challenge than the "*Reaction Time Tests*" or the "*Static Tracking Task*."

A combined score for the "*uncertainty*" introduced by the electrode interface must also be generated ("*Uncertainty Score*"). This is an ordinal score based on the reaction time spread across the conditions highlighted in Section 2.2.1 and the number of unwanted activations during the transitions (see Section 2.2.2).

### *<IP_ADDRESS>. Prosthesis Functionality and Everyday Usage*

The variables characterizing prosthesis "*functionality*" and "*usage*" were split into four key areas as described in Sections "2.3" and "2.4." Of the three possible functional tasks (easy "*A*," medium "*B*," and hard "*C*"), all participants attempted two that were analyzed independently.

Initially, a basic performance evaluation was undertaken. A "*performance score*" was generated based on the scores for the completed movement components of the task (see section 2.3.2) and the task duration (in seconds). Participants were penalized up to a total of 1 s for movement components they failed to complete successfully.

For all trials where the participant completed the "reachto-grasp" component of the task, the hand aperture profile was analyzed to establish the percentage of reach consumed by the "*reach plateau*" period and the "*delay plateau*." Using the methods developed by Thies et al. (2009), variability in the linear acceleration of the forearm was also calculated, for both the "reach-tograsp" component and the full task.

Analysis of the eye tracking data used a coding scheme to record the AOIs on which the gaze was concentrated for every frame, allowing for the time spent in each AOI and the number of transitions between AOIs to be calculated. Furthermore, the time spent looking ahead to the next component of the task was calculated.

Finally, analysis of the activity counts recorded by the Actigraph activity monitors allowed for the calculation of the "*bilateral magnitude*" and "*magnitude ratio*" using the methods described in section "2.4." By combining the raw data with the activity diary, it was also possible to establish the wear time of the prosthesis. For the purposes of this study, the results are reported based on the data recorded throughout the week, irrespective of whether the prosthesis was worn. However, to allow fair comparison of the "*magnitude ratio*" between prosthesis users and stroke patients (Bailey et al., 2015), analysis was also undertaken based only on the periods when the prosthesis was worn. For this secondary analysis, overnight removal of the prosthesis was excluded based on visual assessment of the raw accelerometer data and activity counts from the monitor worn on the prosthesis. Data were excluded from the last activity count on 1 day until the first count on the next day (activity count spikes during these non-wear periods, lasting less than 1 min with at least 10 min of non-use either side, were also excluded). If visual analysis of the raw data showed long periods (>1 h) of no prosthesis activity during the day, these periods were also excluded based on the activity counts, with the assumption that the prosthesis was removed. For more information on the visual analysis, please see the Supplementary Material. Similarly to Bailey's data, the median "*magnitude ratio*" was reported to avoid the effects of skewness.

### *<IP_ADDRESS>. Relationships between Factors Affecting Prosthesis Control and Functionality/Usage*

The early pilot work was not intended to draw conclusions on the relationship between the different factors. However, the main study will aim to establish how measures of "*functionality*" and "*everyday usage*," can be explained by the factors affecting myoelectric prosthesis control. Using multiple regression techniques, factors affecting prosthesis control (i.e., "*EMG skill*" and "*uncertainty*") will be related to measures of "*functionality*" and "*everyday usage*," specifically:

• the sum of the points gained for successful completion of the task,

To further characterize upper limb performance, measures of "*everyday usage*" will be correlated against measures of "*functionality*" collected within the clinic. These may include association of the "*magnitude ratio*" and wear time with:

Based on the findings of these analyses, it should be possible to establish the relative contribution of the factors affecting prosthesis control to each measure of "*functionality*" and "*everyday usage*."

### 3. INITIAL PILOT STUDY RESULTS AND DISCUSSION

In this section, we use early results from initial pilot work with anatomically intact subjects using a prosthesis simulator and prosthesis users to demonstrate the feasibility of this protocol. Data collected from two prosthesis users (both male, age 44–45, with congenital limb absence, and 1.5–35 years using a myoelectric prosthesis) and one anatomically intact subject using a prosthesis simulator (male, age 21, no experience) are presented.

### 3.1. Data Collection

The data collection period lasted between 4 and 5 h, including breaks, which was longer than desired, however, the protocol included tests that have since been removed. In the format presented above, the protocol would, therefore, be expected to last less than 4 h. For our study in this reduced format, the first 40 min consisted of finding the "*ideal*" electrode placement (see 2.1.1) and undertaking the "*tracking tasks*" (see 2.1.3). The "*reaction time tests*" for "*EMG skill*" analysis (see 2.1.2) took 20–30 min while the "*uncertainty tests*" (see 2.2.1 and 2.2.2) lasted a further 50–60 min. Finally, 40–50 min were spent setting up and undertaking the "*cylinder task*" (see 2.3). Breaks were provided at set points in the protocol to ensure participants' attention was maintained.

### 3.2. Initial Analysis

3.2.1. Reaction Time Tests for the Analysis of EMG Skill During the "*reaction time tests*" (see section 2.1.2), data were recorded from the goniometer both before and after the stimulus (LED) was presented. **Figure 6** shows example data recorded during the second of stimulus presentation. The red circle identifies the time point identified as the moment of hand movement onset in response to stimulus presentation. More detail on the algorithms employed to identify movement onset are presented in the Supplementary Material.

It is widely accepted that the mean reaction time for collegeaged individuals undertaking simple reaction time (SRT) tests with light-based stimuli is around 190 ms (0.19 s) (Kosinski and Cummings, 2004). During tests where the stimulus determines the reaction (CRT tests), times are often slower; exact speeds depend on the test. Moreover, the inherent delay introduced within the prosthesis would be expected to produce prosthesis reaction times that are longer than the anatomical reaction times. Initial results demonstrated measured SRT of 270–290 ms (**Figure 7**); furthermore, an increase in reaction time was seen

FIGURE 6 | Analysis of goniometer data recorded after the LED stimulus presentation in the reaction time experiment. The red marker signifies the point identified as the onset of movement.

difference between the mean CRT and the mean SRT.

between the Simple and Choice Reaction Times of 45–100 ms ("*Decision Time*"). It is worth noting that reaction times and consistency improve after first introduction to a new task (Kosinski and Cummings, 2004); this may show as a learning effect in the "*decision time*" over the small number of repeats. However, we decided not to randomize the order so that all participants underwent the same sequence of testing: the SRT first, then the CRT (see section 4). The "*decision times*" presented in **Figure 7**

suggest that Prosthesis User 2 was less skilled at deciding which muscles to activate than the other two participants.

### 3.2.2. Tracking Tasks for the Analysis of EMG Skill

The "*static tracking task*" (see section 2.1.3) assessed the participant's ability to maintain a specified signal level. This task demonstrated that different levels of "*EMG skill*" can be measured and did not show a ceiling effect; i.e., no participant achieved 100% (**Figure 8A**).

The simulator user appeared to perform better than either of the prosthesis users. It is interesting to note that during a sustained contraction, both prosthesis users demonstrated co-contraction or crosstalk for one of the two muscle groups (**Figure 8B**).

All participants were able to complete the "*dynamic tracking task*" (see section 2.1.3). Two participants performed better when only one car (muscle signal) was under assessment (Part 1), with a 20–40% higher success rate than when presented with 2 cars (Part 2). Prosthesis User 2, who demonstrated large amounts of co-contraction or crosstalk when activating the close signal (**Figure 8B**), did not fit this trend, instead a 10% improvement was seen in the success rate for the close signal for Part 2, and a 60% reduction in success with the open signal. During this second part of the dynamic task when two cars were being controlled, the participant was unable to relax the open signal while contracting the close muscle. This meant that the "open car" was guaranteed to "crash" for at least 50% of the gaps. It is possible that this participant, therefore, changed strategy to concentrate on the easier to control close signal. Alternatively, it is possible that this participant was unable to visually track the two cars and struggled with focusing equally on controlling each signal. One further suggestion is that this links with the reaction time results, which showed that this participant found deciding which muscle to activate harder than the other participants.

At this stage, it is not possible to draw any firm conclusions based on these results. However, we have demonstrated that both "*tracking tasks*" offer the possibility of differentiating between different levels of skill in controlling the EMG signal. Based on these tracking tasks, the simulator user demonstrated a higher level of skill than the two prosthesis users.

### 3.2.3. Effects of Electrode Interface Condition on EMG Transduction

Both prosthesis users experienced some difficulty in completing the tasks designed to measure the extent of "*unpredictability*" in transduction of the EMG signal leading to "*uncertainty*" ("*uncertainty tests*," see section 2.2). User 1 had a good level of control over the prosthesis, and was able to operate it as desired, however, the residual limb was very short. Consequently the participant found the addition of the 500 g mass fairly difficult to hold, reporting discomfort at the elbow. User 2 had a longer residual limb and reported feeling the additional load in his shoulder muscles. Both participants were happy to undertake the task with a 500 g load attached to the hand but would have struggled to support the prosthesis if the mass was much heavier.

The anatomically intact participant using the simulator did not exhibit any clear difficulty with completing the reaction time portion of the task (Section 2.2.1), however, when moving between the arm postures (see section 2.2.2), four unwanted activations occurred (two with the "*ideal*" interface condition and two with the "*additional load*"). The reaction time data from Prosthesis User 1 showed a large amount of variation in reaction times for all three interface conditions (**Figure 9**); however, this user did not experience any unwanted activations of the hand. Finally, Prosthesis User 2 only experienced a small amount of variability in reaction times (**Figure 9**) when undertaking the task with the "*ideal*" electrode interface condition (electrodes bandaged to the limb). However, when the socket was introduced ("*normal*" interface condition and "*additional load*"), the participant encountered a large amount of difficulty in getting the prosthesis to react as desired. For 13 of the 20 open tasks, the hand closed when the participant attempted to open it; and for those tasks where the participant did manage to open the

hand, the movement trajectory was not smooth. **Figure 10** shows a comparison of the goniometer data between Prosthesis User 2 and the other two participants. It is worth noting that each participant used a different prosthetic hand for this assessment and that the total aperture for the hand used by Prosthesis User 2 was much smaller than for the other two participants, hence the difference in range. Moreover, Prosthesis User 2 had a much looser socket fit than User 1. Consequently, as the "open muscle" contracted, the limb seemed to push against the socket moving the "close electrode" away from the skin and activating the close movement instead. This "*unpredictability*" in socket fit was also highlighted by the seven unwanted activations when transitioning between the different arm positions.

#### 3.2.4. Functionality Assessment

All participants began with the medium difficulty task ("*task B*"); completion of the task ranged from 100% (Prosthesis User 1) to less than 50% (Prosthesis User 2) of trials. Both Prosthesis User 1 and the simulator user completed over 80% of trials of "*task B*" and, therefore, moved on to the harder task ("*task C*"). Prosthesis User 2 experienced difficulty grasping the cylinder, and often dropped it as he rotated it to the horizontal. When attempting the easier task ("*task A*"), he completed 90% of the trials; however, during two of these trials, he missed the cylinder on the first attempt of "reach-to-grasp."

As introduced in Section "2.3.1," data were collected using wrist and chest-mounted IMUs, an electronic goniometer and an eye tracker. The systems were synchronized using the button press; pilot data demonstrated that synchronization was successful. The task durations, based on the button timestamps, illustrate that Prosthesis User 2 performed the medium difficulty task ("*task B*") at a slower rate than the other two participants (**Figure 11**). Prosthesis User 1 was the most consistent regarding

FIGURE 11 | (A) Mean task duration for each of the difficulty levels (Easy "A," Medium "B," and Hard "C"), (B) mean aperture "reach plateau" length as a percentage of the reach phase, (C) mean aperture onset delay as a percentage of the reach phase.

the time taken to perform the task, and as noted above, the most successful. Furthermore, Prosthesis User 1 demonstrated aperture patterns more similar to the healthy norms with a shorter "*reach plateau*" in the reach phase (**Figure 11**); onset delay was similar across the three participants (**Figure 11**).

As highlighted above, Prosthesis User 2 struggled to complete "*task B*," dropping the cylinder during rotation of the arm; the screenshots in **Figure 12** summarize the technique employed by the participant to overcome this "*unpredictability*." Unlike Prosthesis User 1 and the simulator user, Prosthesis User 2 waited until the last minute, when the cylinder was in contact with the tube, before rotating the cylinder to the horizontal. The participant's "*uncertainty*" as to how the hand would respond is highlighted in the results of the eye tracking. The eye tracking videos (**Figure 12**) were individually coded frame by frame to establish where the participant was looking. As can be seen in the images at the top of **Figure 12**, both prosthesis users looked at their hand during "reach-to-grasp," however as can be seen in **Figure 13**, there were noticeable differences in the gaze patterns of these two users. Prosthesis User 2 spent the majority of the time looking at the hand and the cylinder, tracking its movement, while Prosthesis User 1 showed a higher level of confidence in the hand, looking ahead to the cylinder and the tube. During the "reach-to-grasp" component of the task, Prosthesis User 1 looked

FIGURE 12 | Example eye tracking video – the crosshair shows the point of gaze fixation. Top: both Prosthesis Users looked at the hand at a point in the reach to check their hand aperture. Bottom: the different strategies employed to complete "task B" can be seen – left: simulator user, middle: Prosthesis User 1, and right: Prosthesis User 2 – Prosthesis User 2 struggled to complete this task and would drop the cylinder when the arm was brought to the horizontal, therefore, he delayed this movement.

ahead of the hand for 76% of the time, while Prosthesis User 2 relied on looking at the hand for over 50% of the time.

### 3.2.5. Everyday Usage

As explained in Section "2.4" participants were asked to undertake activity monitoring over the period of 1 week. For the purposes of this pilot study, data were only collected for the two prosthesis users; however, to check the methods against Bailey's data (Bailey et al., 2015) (see section 2.4), one separate anatomically intact participant underwent activity monitoring using their anatomical arms. The anatomical results echoed Bailey's findings with symmetrical use across the two arms represented by a median "*magnitude ratio*" of 0.11 (IQR = 3.28) (**Figure 14A**).

At present, no algorithm exists allowing for differentiation between non-wear and passive-use of the prosthesis using the wrist worn Actigraph monitors. Consequently, participants were asked to complete activity diaries, which subsequently showed that Prosthesis User 2 only wore his device for 3 of the 7 days,

count of the number of data points. (A) Top: bilateral arm use for anatomically intact control subject. Arm use is symmetrical across both arms, regardless of limb dominance. (B) Middle: bilateral arm use for Prosthesis User 1. (C) Bottom: bilateral arm use for Prosthesis User 2.

while User 1 wore his all week. This non-wear is reflected in the activity monitor data with purely unilateral use of the anatomical arm on these days and no activity counts for the prosthesis. From the activity diaries, we know that both participants generally wore their prosthesis for 10 h or more on the days when they were worn. It is, therefore, important that the data are collected over the week long period to ensure that representative data for each user is collected.

**Figures 14B,C** illustrate that both prosthesis users rely on their anatomically arm to a greater extent than the stroke patients participating in Bailey's study (Bailey et al., 2015). Both prosthesis users demonstrated median "*magnitude ratios*" of −7 [IQR = 5.40 (Participant 1) IQR = 0 (Participant 2)] (unilateral use of the intact arm) similar to the group of Bailey's stroke participants who rely most on their non-paretic arm. However, when only the data collected while the prosthesis was worn is included in the comparison, the median "*magnitude ratios*" reduce to −2.55 (IQR = 6.42) for Prosthesis User 1, and −2.42 (IQR = 6.76) for Prosthesis User 2. It is interesting to note that although Prosthesis User 1 wore the device for more hours during the week, both participants demonstrated similar median "*magnitude ratios*." Furthermore, it is notable that the "*bilateral magnitude*" of User 1's activity was of a level much closer to the stroke patients, while User 2 demonstrated activity to the same magnitude as Bailey's healthy controls.

### 4. LIMITATIONS AND FUTURE WORK

For the purposes of this study, the prosthesis control chain has been characterized up to the point of EMG signal transduction. In reality, however, further to the "*EMG skill*" and "*uncertainty*" addressed above, an inherent electromechanical delay will be introduced by the prosthesis itself, which will also impact on the ease of controlling the device. This delay is a culmination of the delays introduced through processing of the EMG signals and stiction/backlash in the prosthetic hand mechanisms. The measurement of these delays is complex requiring artificial activation of the electrodes. Previous work has been undertaken to calculate the optimal controller delay in a prosthesis (Farrell and Weir, 2007); however, the delays in clinically available prostheses are not available. Future work would involve integrating the measurement of electromechanical delays into the protocol detailed above.

Other limitations are the assumptions that have been made with respect to the "*Reaction Time Tests*" (Section 2.1.2). Reaction time experiments involving simple and choice reaction times would normally be randomized, and undertaken in large numbers, to overcome learning or attentional effects. This study involves the comparison of performance in these tasks between participants, therefore, it is important that all participants experience the same tasks in the same order. Furthermore, time constraints limit the number of repeats that can be undertaken. Although different participants may learn at different rates, it is assumed that as the task is novel to all participants the results will be comparable.

Furthermore, the tube used in the "*cylinder task*" is transparent, meaning that when the cylinder is within the tube it can be difficult to identify whether the participant is looking at the cylinder or the tube (likely both). Similarly when the gaze is on the GCA of the cylinder, as the hand approaches and blocks the view, it is not clear whether the AOI should be coded as the hand or the GCA. An inter-rater reliability study will be undertaken to assess the proposed approach to coding the gaze data.

As discussed in Section "2.4," the analysis methods for the assessment of everyday upper limb use were borrowed from the study by Bailey et al. (2015). A limitation of this method is that it does not inform on actual hand use. Therefore, it is not possible to confirm whether the activity counts recorded relate to the prosthetic hand being used in an active or passive manner. For future studies, it would, therefore, be worth including a system to also monitor hand movements. This approach was advocated by Sobuh et al. (2010) and a recent paper by Rowe et al. (2014) demonstrated the potential for a similar approach in the monitoring of anatomically intact upper limb movements.

Finally, reliability and validity of the experimental setups and corresponding outcome measures need yet to be explored. Reliability can be established through a test–retest study in a subset of our planned cohort. Validity of measures, where possible, may be investigated via comparison to related, established measures, for example, by comparing functional measures during "*cylinder task*" performance to SHAP and/or Box and Blocks test scores. For validation of measures characteristic of prosthesis control, we may utilize a known-groups assessment to investigate their sensitivity to distinguish between novice and experienced myoelectric prosthesis users, and we could further conduct a responsiveness study in novice myoelectric prosthesis users to identify whether an individual measure of prosthesis control responds to effects of training to perform the corresponding experimental set up of the protocol.

### 5. CONCLUSION

In this paper, we presented a protocol for the assessment of user skill in controlling EMG signals ("*EMG skill*") and "*unpredictability*" in the acquisition of these signals. These are to be assessed against overall user "*functionality*" and "*everyday usage*" of the myoelectric prosthesis. To demonstrate the protocol, results of initial pilot work were presented.

Pilot work and initial analysis of the results suggest that this protocol will be able to successfully identify differences in the "*EMG skill*" level of participants and characterize the "*unpredictability*" at the electrode interface. Data have been successfully collected for each aspect of the functional task that will allow analysis of how each control factor affects "*functionality*." Furthermore, analysis of the activity monitoring data will allow assessment of control factors against "*everyday usage*."

Although the results presented are not sufficient to draw firm conclusions, Prosthesis User 2 appeared to demonstrate a lower level of "*functionality*" than User 1, which could be attributed to either of the control factors at this stage. By collecting data across a larger cohort of prosthesis users, it should be possible to identify the relative contributions of these factors.

Finally, although the protocol is relatively long, pilot participants were provided with regular breaks and were happy with the distribution of the tasks; the length of the study was not felt to be excessive. Performing all tasks in a single test session (including breaks to avoid fatigue) has the advantage that it facilitates protocol completion in myoelectric prosthesis users, who are largely part of the working population and, hence, could prove difficult to schedule on multiple occasions within a reasonable time frame. Nevertheless, each experimental setup has been designed in such a way that it could be performed in isolation of other parts of the protocol, providing useful insights on the isolated factor the experiment is concerned with. Hence, while the complete protocol may be predominantly used by researchers due to its complexity, individual parts could be adopted by clinicians to support their decision making.

### ETHICAL CONSIDERATIONS

Ethical approval for the work reported here was granted by the University of Salford ethics committee (*REF: HSCR 15-130*). All participants provided informed written consent.

## DISSEMINATION OF RESULTS

It is intended that the results of the main study will be published in peer-reviewed journals and will be uploaded as part of the lead author's PhD thesis onto the University of Salford SEEK website. Additionally, it is intended that the results of this early work and the final study will be presented at conferences both in the UK and internationally.

### AUTHOR CONTRIBUTIONS

The study was initially conceived by LK. All authors contributed to the design of the study, which was implemented by AC. All authors were involved in the drafting and approving of the manuscript.

### ACKNOWLEDGMENTS

The authors would like to acknowledge the contributions of K Hesselink (Universiteit Twente) to the initial design of the "*cylinder task*," and JC de Vries (Universiteit Twente) who contributed to its subsequent refinement. Dr. J Gardiner (University of Salford) should also be recognized for valuable suggestions to improve the protocol design.

### FUNDING

AC is funded by the University of Salford as part of the Graduate Teaching Studentship scheme, without which this work would

### REFERENCES

not have been possible. Furthermore the Ottobock Myohand VariPlus Speed used in this study was funded through an IPEM innovation and research award.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online athttp://journal.frontiersin.org/article/10.3389/fnbot.2016.00007

**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 © 2016 Chadwell, Kenney, Thies, Galpin and Head. 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.*

# Corrigendum: The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control

#### Alix Chadwell, Laurence Kenney\*, Sibylle Thies, Adam Galpin and John Head

Centre for Health Sciences Research, University of Salford, Salford, United Kingdom

Keywords: prosthesis, myoelectric, activity monitoring, control, upper limb, functionality assessment

#### **A corrigendum on**

#### **The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control**

by Chadwell, A., Kenney, L., Thies, S., Galpin, A., and Head, J. (2016). Front. Neurorobot. 10:7. doi: 10.3389/fnbot.2016.00007

#### Edited and reviewed by:

Claudio Castellini, Robotik und Mechatronik Zentrum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Germany

> \*Correspondence: Laurence Kenney<EMAIL_ADDRESS>
Received: 18 February 2018 Accepted: 19 March 2018 Published: 03 April 2018

#### Citation:

Chadwell A, Kenney L, Thies S, Galpin A and Head J (2018) Corrigendum: The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control. Front. Neurorobot. 12:15. doi: 10.3389/fnbot.2018.00015 In the original article, there was an error. The equation for the Magnitude Ratio presented in the Methods and Analysis was incorrect and should be written ln(VMNonDom/VMDom).

A correction has been made to Methods and Analysis, Everyday usage (Section 2.4), paragraph 1: Current methods of quantifying everyday prosthesis use involve self-report (Roeschlein and Domholdt, 1989; Sherman, 1999; Gallagher and MacLachlan, 2000; Raichle et al., 2008), which is known to be prone to recall and bias errors (Metcalf et al., 2007; Brown and Werner, 2008). Accelerometer-based activity monitoring (Noorkõiv et al., 2014) provides an opportunity to observe actual prosthesis use outside of the clinical environment; however, to date no studies have been published on a cohort of upper limb prosthesis users. We have adapted a protocol developed for stroke patients (Bailey et al., 2015). This research involved participants wearing an activity monitor (Actigraph GT3X+) on each of their wrists while they went about their normal daily activities. The Actigraph monitors provide continuous logging of raw accelerometer data (sampled at 30 Hz). The data are downloaded using proprietary software, filtered, and down sampled to 1 Hz. The processed data are expressed as activity counts (0.001664 g/count) (Actigraph Corp., 2015), which are converted into vector magnitudes (sum of the counts along each axis p x <sup>2</sup> + y <sup>2</sup> + z 2 ). For each second of data, Bailey et al. (2015) combined the vector magnitudes from each of the two wrist worn monitors (dominant and non-dominant arm) to inform on the magnitude of activity across both arms, expressed as the "bilateral magnitude" (VMDom + VMNonDom), and the contribution of each arm to the activity, expressed as the "magnitude ratio" [ln (VMNonDom/VMDom)].

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way.

### REFERENCES

Actigraph Corp. (2015). ActiGraph White Paper: What Is a Count? Pensacola, FL.

**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 Chadwell, Kenney, Thies, Galpin and Head. 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.

*Irfan Hussain1 \*, Giovanni Spagnoletti1 , Gionata Salvietti1,2 and Domenico Prattichizzo1,2*

*1Department of Information Engineering and Mathematics, Università degli Studi Siena, Siena, Italy, 2Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy*

In this paper, we propose a novel electromyographic (EMG) control interface to control motion and joints compliance of a supernumerary robotic finger. The supernumerary robotic fingers are a recently introduced class of wearable robotics that provides users additional robotic limbs in order to compensate or augment the existing abilities of natural limbs without substituting them. Since supernumerary robotic fingers are supposed to closely interact and perform actions in synergy with the human limbs, the control principles of extra finger should have similar behavior as human's ones including the ability of regulating the compliance. So that, it is important to propose a control interface and to consider the actuators and sensing capabilities of the robotic extra finger compatible to implement stiffness regulation control techniques. We propose EMG interface and a control approach to regulate the compliance of the device through servo actuators. In particular, we use a commercial EMG armband for gesture recognition to be associated with the motion control of the robotic device and surface one channel EMG electrodes interface to regulate the compliance of the robotic device. We also present an updated version of a robotic extra finger where the adduction/ abduction motion is realized through ball bearing and spur gears mechanism. We have validated the proposed interface with two sets of experiments related to compensation and augmentation. In the first set of experiments, different bimanual tasks have been performed with the help of the robotic device and simulating a paretic hand since this novel wearable system can be used to compensate the missing grasping abilities in chronic stroke patients. In the second set, the robotic extra finger is used to enlarge the workspace and manipulation capability of healthy hands. In both sets, the same EMG control interface has been used. The obtained results demonstrate that the proposed control interface is intuitive and can successfully be used, not only to control the motion of a supernumerary robotic finger but also to regulate its compliance. The proposed approach can be exploited also for the control of different wearable devices that has to actively cooperate with the human limbs.

#### *Edited by:*

*Michael Wininger, University of Hartford, USA*

#### *Reviewed by:*

*Alejandro Linares-Barranco, University of Seville, Spain Andrew Paul Tosolini, University College London, UK*

#### *\*Correspondence: Irfan Hussain*

<EMAIL_ADDRESS>
*Received: 25 July 2016 Accepted: 24 October 2016 Published: 11 November 2016*

#### *Citation:*

*Hussain I, Spagnoletti G, Salvietti G and Prattichizzo D (2016) An EMG Interface for the Control of Motion and Compliance of a Supernumerary Robotic Finger. Front. Neurorobot. 10:18. doi: 10.3389/fnbot.2016.00018*

Keywords: wearable robotics, supernumerary robotic fingers, compliance control

### 1. INTRODUCTION

Wearable robotic devices have been mainly used in substitution of lost limbs [e.g., prosthetic limbs (Carrozza et al., 2004)] or for human limb rehabilitation [e.g., exoskeletons (Pons, 2008)]. Besides traditional wearable robotic structures, a very promising research direction aims at adding robotic extra limbs to humans, rather than substituting or enhancing the human limbs (Davenport et al., 2012; Wu and Asada, 2014). The advantage of using wearable robotic extra limbs is twofold. From one side, this addition can enable humans to augment their capabilities (Llorens-Bonilla et al., 2012). On the other side, extra limbs can compensate the missing abilities of impaired limbs, e.g., in case of chronic stroke patients (Salvietti et al., 2016).

We recently started to investigate how an extra (supernumerary) robotic finger can be used in cooperation with the human hand. We mostly focus on two possible applications: compensate the missing abilities of stroke patients with a paretic hand and augment the human healthy hand so as to enhance its capabilities. Concerning grasp compensation in stroke patient, we noted that, in last decade, many wearable devices have been proposed, especially for hand rehabilitation and functional recovery (Heo et al., 2012; Lum et al., 2012). However, only 5–20% of patients show a complete recover of upper limb 6 months after the stroke (Nakayama et al., 1994). We have, thus, proposed a wearable extra finger device that allows the patient to regain the grasping function of the hand when the deficit is stabilized (Hussain et al., 2015b; Salvietti et al., 2016). The main idea was to have the robotic finger and paretic arm acting as the two parts of a gripper to hold an object. The human user was able to control the flexion/ extension of the robotic finger through a switch placed on a ring, while being provided with vibrotactile feedback about the forces exerted by the robotic finger on the grasped object. In Salvietti et al. (2016), we introduced an EMG interface that captures the frontalis muscle activation to control the finger flexion/extension. Finally, in Hussain et al. (2016), we proposed an underactuated compliant extra finger as well as an EMG interface embedded in a cap. Concerning augmenting human healthy hand, in Prattichizzo et al. (2014a), we presented a preliminary version of a robotic extra finger showing how this wearable device is able to enhance grasping capabilities and hand dexterity in healthy subjects. In Prattichizzo et al. (2014b), we presented an objectbased mapping algorithm to control robotic extra limbs without requiring explicit commands by the user. The main idea of the mapping was to track human hand by means of dataglove and reproduce the main motions on the extra finger. Although the earlier presented works on extra-robotic fingers clearly report the impact of the research, the presented robotic devices and their control interfaces are not enough general. In fact, the proposed systems could manage only few inputs (e.g., few predefined closing trajectories), and no solutions have been proposed to modulate the compliance of the robotic finger so as to control the force on the grasped object. Since supernumerary robotic fingers are supposed to closely interact and perform actions in synergy with the human limbs, the control principles of extra finger should have similar behavior as human's ones. Humans can dynamically change their arm stiffness depending on the environment and the tasks being executed (Ajoudani et al., 2012). For instance, stiffness can be increased by muscle cocontraction when we want to make a precise positioning, or when we hold heavy loads. So that, making the actuators and sensing capabilities of the robotic extra finger compatible to implement stiffness regulation control techniques is of primary importance (Hogan, 1985). Second, we believe that the user should directly control through an interfaces of the stiffness of robotic fingers.

The main contribution of this work is the development of a novel EMG interface that can be used to control both the motion of the supernumerary robotic finger and its compliance and thus the tightness of the obtained grasp. In particular, we relate different finger motions to different gestures of the human hand. We used a commercial EMG interface (Myo Armband, ThalmicLab) for hand gesture recognition. For the compliance control, we used a dedicated surface one bipolar EMG channel to read the user biceps signal. The separation of the two EMG reading allows the user to better control independently grasp tightness and device motion. We also present an updated version of the prototype of robotic extra finger where the adduction/abduction motion is realized through ball bearing and spur gears mechanism. The proposed system can be used both by patients for grasp compensation and by healthy subjects for grasp augmentation. We performed a pilot study to demonstrate the feasibility of the approach both with healthy hand for augmenting its abilities and simulated paretic hand to compensate missing grasp abilities. We involved four healthy subjects to perform two different sets of experiments involving the augmentation of a healthy hand or the compensation of a simulated paretic hand. In both cases, the interface resulted sufficient to effectively control the extra-robotic finger so as to fulfill the proposed task. In all the experiments, the wearable device was worn in one arm, whereas the control interface was worn on the other. In fact, while healthy subjects could potentially wear the interface on the same arm where the device is worn, patients cannot properly control hand motion and muscle contraction in their paretic upper limbs. Use the healthy arm is a possible solution as well as delocalizes the EMG reading in another part of the body, see, e.g., Hussain et al. (2016). Note that the hand gestures are necessary only to select a predefined behavior of the device, so it is not necessary to keep a certain gesture for long period. This is important in bimanual tasks where both hands can be used.

The rest of the paper is organized as follows. In Section 2, we present the materials and methods. In particular, the details of the design and development of the proposed supernumerary robotic finger and the proposed EMG control interfaces are explained in details. In Section 3, the experiments using the proposed system are presented. The results are detailed in Section 4 and discussed in Section 5. Finally, in Section 6, conclusion and future work are outlined.

### 2. MATERIALS AND METHODS

### 2.1. The Supernumerary Robotic Finger

The proposed supernumerary robotic finger is composed of modules connected to partially resemble the human finger mechanical structure. Human hand fingers, excluding the thumb, consist of four phalanges connected by three joints (Jones and Lederman, 2006). The structure of the thumb is different since it has two joints at the base for the anterposition or retroposition combined with the radial or palmar abduction motions. The other fingers are capable of both adduction–abduction and flexion–extension motions. The finger's kinematic model is typically approximated by using simple revolute joints. This approximation is an effective means of modeling, as these are, in fact, the same as compared to proximal and distal joints of humans. The proximal and distal interphalangeal articulations can have only flexion/extension motion capabilities and typically are represented with a single DoF revolute joint. The metacarpal joints have both adduction/abduction and flexion/extension motion capabilities and can be modeled as a 2-DoFs joint that is composed of two revolute joints with orthogonal rotation axis (universal joint). We designed the kinematic structure of the robotic extra finger such that one motor is adopted to actuate each DoF of the robotic finger so as to replicate the flexion/extension motion of the human finger. While, at the robotic finger base, two motors realize the adduction–abduction and flexion–extension motion to replicate metacarpal joint. We used four modules in a pitch–pitch configuration for the flexion–extension motion of the finger so as to approximate the average length of the whole hand (Taylor and Schwarz, 1955). The adduction/abduction motion of base joint is obtained using spur gears that allows to transmit motion and power. One of the spur gear is mounted on the shaft of the servo motor, whereas the other is placed on the base of the finger. We used bearings to decrease the friction during rotation.

The finger design is based on the principle of modularity. Each module consists of a servomotor, a 3D printed structure (Acrylonitrile Butadiene Styrene, ABSPlus, Stratasys, USA) and a soft rubber part mounted on front to increase the friction at the contact area. The actuators used are the HS55 MicroLite servo motors. The modules are connected so that one extremity of each module is rigidly coupled with the shaft of the motor through screws, while the other has a pin joint acting as revolute joint. The exploded view and the prototype of the device are shown in **Figure 1**.

The servo motors are pulse width modulation (PWM) controlled. The PWM signals are generated by a microcontroller At-mega 328 installed on an arduino nano board. The portability and wearability of the device is improved by enclosing all the electronics circuitry in a 3D printed housing which is attached to the finger base support. An external battery pack (5 V) is used to provide power to the actuators. Technical details on the device are summarized in **Table 1**.

### 2.2. The EMG Control Interface for the Supernumerary Robotic Finger

As explained in the introduction, we combined the EMG signals associated with the activation of more muscles for the proposed interface. In particular, we used two EMG interfaces on the arm, one to record the continuous EMG amplitude aiming to regulate the compliance of the device and the second

FIGURE 1 | On left, the exploded cad view, whereas on right, the prototype of the robotic extra finger. Four modules are used for the flexion/extension motion, while the revolute joint based on bearings and spur gears mechanism at the finger base is used for the adduction/abduction motion. The device can be worn on the forearm through an elastic band.

TABLE 1 | The technical details of supernumerary robotic finger.

to recognize different hand gestures to be associated with the motion of the robotic finger. Both EMG interfaces are placed on one of the arms, one at the biceps and other at the forearm, while having the robotic finger on the other arm as shown in **Figure 2**. We developed the circuit acquisition and signal conditioning board for one channel EMG electrodes to measure continuously the biceps muscle EMG signal variations. We used the Myo Armband to recognize the gestures at forearm position.

**Figure 3** shows the block diagram of the proposed system. Both, EMG one channel interface and Myo Armband are connected to a computer through Bluetooth communication. The PC runs MATLAB that is used to process the EMG signal for the compliance control. In order to stream data from Myo Armband to the robotic finger, we used MyoMex. The PC communicates with the robotic device controller (Arduino) through serial communication which in turn controls the motion and compliance of the supernumerary robotic finger.

Section 2.2.1 describes the development of the acquisition and signal conditioning board for one channel EMG interface followed by the compliance regulation of the robotic device through the amplitude variation in the acquired biceps EMG signal. In Section 2.2.2, we describe the gesture recognition through the Myo Armband and their association with the motion control of the supernumerary robotic finger through a finite state machine (FSM).

### 2.2.1. One Channel EMG Electrodes Interface and Robotic Device Compliance Regulation

We used non-gelled reusable silver/silver-chloride electrodes for the EMG one channel interface. These are recommended for biopotentials recording since they present the lowest noise interface (Merletti et al., 2009). The design and development of the EMG signal acquisition board is carried out, while considering

FIGURE 3 | Block diagram of complete system. On top left, the block diagram of EMG one channel interface is shown, where (a) surface electrodes, (b) snap leads, (c) acquisition board, and (d) control board. On top, right, the myoarm band with its major components (e) logo LED, (f) status LED, (g) expandable flex, (h) micro USB charging port, and (i) electrical sensor.

the requirements associated with bandwidth, dynamic range, and physiological principles. The typical EMG waveform is characterized with a spectral content between 10 and 250 Hz with amplitude up to 5 mV, depending on the particular muscle (Merlo and Campanini, 2010). The first stage of the signal conditioning board is developed by using an instrumentation amplifier (INA333) which offers an high common-mode rejection ratio (110 dB @ *G* ≥ 10), while the second stage contains a low-noise high speed operational amplifier (AD869x) to perform band-pass filtering and amplification of the acquired EMG signal. **Figure 4** shows the block diagram of the implemented EMG circuit board. Three electrodes are interfaced to the board; two of them (*VIN+* and *VIN*−) are connected to the inputs of an instrumentation amplifier (In-Amp) and third one called "reference electrode" is connected to a mid-supply reference voltage (*Vss* = 1.65 V). This configuration improves the quality of EMG signal acquisition as it increases the common-mode rejection ratio (CMRR). The first stage of the EMG board is an In-Amp with an additional stage of AC coupling. This configuration allows a precise control of DC levels rejecting undesired DC offset voltage introduced by electrode–skin interface. The DC component is subtracted by feeding the output signal back to the reference input of the In-Amp, by an integrator feedback network, which results in the first-order high-pass response. The second stage of the EMG board is a 4th order low-pass Butterworth filter. An active topology (a Sallen–Key circuit implementation – 4th order low-pass filter cascading two stages of 2nd order) was chosen to get a better performance and less complexity than a passive one. The acquired EMG signal is sampled at 1 kHz (double EMG band) to avoid aliasing.

The reference value of received EMG was normalized using maximum voluntary contraction (MVC) technique (Farina and Merletti, 2000). This solution avoids the problems related to the high influence of detection condition on EMG signal amplitude. In fact, amplitude can greatly vary between electrode sites, subjects, and even day-to-day measures of the same muscle site. We implemented an autotuning procedure based on the MVC in order to better match the user-dependent nature of the EMG signal. The implemented MVC routine consists of a 3-s time window in which the user slowly starts increasing the contraction of the biceps muscle to reach their maximum effort.

placed on the biceps muscle.

**Figure 5A** shows the relation between EMG (percentage of MVC) signal at biceps and time (milliseconds). The relationship between EMG signal and muscle tension is non-linear. The MVC value itself is not calculated as a single peak data point because that would allow too much variability. In order to obtain a more stable reference value, we have implemented an algorithm using a sliding window technique of 500 ms duration to compute the mean amplitude of the highest signal portion acquired during the 3-s time window.

The technical details of the EMG acquisition board are listed in **Table 2**.

The EMG signal acquired through the developed one channel interface is used to control the stiffness of each module of the robotic device through the implemented control scheme based on servo motor.

In the following, we will explain how stiffness regulation has been obtained using servomotors. Generally, in active compliance control framework, the equation relating the motor torque to its position is given by

$$\pi = k \Delta\_q = k(q\_{\text{des}} - q\_m)$$

where *qdes* is the desired (reference) joint position, *qm* is the measured (current) joint value, and *k* is the stiffness constant (Siciliano et al., 2010). Note that the compliant (or stiff) behavior of the joint is achieved by virtue of the control, differently from what happen in mechanical systems with a prevalent dynamics of elastic type. This controller is typically used with actuator that can be torque controlled. Servo motors are position controlled actuators where it is not possible to directly command the exerted torque. A small reference position variation in the clockwise direction is counterbalanced by a large amount of torque in the counterclockwise direction to compensate for this. This behavior is regulated by the controller embedded in the servo motor and cannot be modified. This torque–position relationship defines the standard stiffness of the servo motor (*kc*) that cannot be changed by the user. The only servomotor parameter that can be commanded is its desired position *qdes*. We considered that, at time instant *t*, the desired position for the *i*-th servomotor is obtained as

*q t q t q t des*, , *i m i i* ( ) = − () () 1 1 + ∆ − ,

where

$$
\Delta q\_i(t) = k\_d k\_\varepsilon (q\_{des} - q\_m). \tag{1}
$$

The scaling factor *kd* is introduced to modulate the position error. In order to vary the parameter *kd*, we used the EMG signal acquired at the user biceps. In particular, the range of EMG signals was linearly mapped in the range 0.4–3 of parameter *kd*. In **Figure 5B**, a plot of the relation between biceps contraction and commanded displacement is reported for one module. In presence of a rigid grasped object, the measured positions of the extra finger joints do not change due to the object constraints. So that, changing the desired position of the servomotors through the scaling factor, we can control the force exerted by the device onto the object. In other words, changing the value of *kd*, it is possible to command a position of the module that results in a higher force applied onto the object.

In order to regulate the stiffness between modules, we set priorities. We considered two distinct cases. If only the fingertip module is in contact with the object, all the other modules change their stiffness accordingly. This solution allows to control the stiffness of modules that are not in contact

#### TABLE 2 | Technical details of EMG signal acquisition and conditioning board.

FIGURE 5 | On left, (A) the maximum voluntary contraction (MVC) proportional to the biceps muscle contraction is shown. While on right, (B) the graph between Δ*q* and percentage of MVC for different values of *kd* is plotted.

with the object in precision grasps. In power grasps, in order to obtain suitable contact points, we set different priorities according to the position of the module in the finger. If the fingertip module comes in contact first, the remaining modules change their stiffness accordingly. If another module comes in contact first, modules below to it regulate their stiffness, while the module above no. The same methodology is followed for other intermediate modules. The contact of a module is detected comparing the desired angle commanded to the servo motor (*qdes*) with the actual position read by the encoders (*qm*). When Δ*q* = ||*qdes* − *qm*|| overtake a predefined threshold a contact is recognized. After the contact is achieved, the compliance can be regulated.

### 2.2.2. EMG Armband Gesture Recognition and Robotic Device Motion Control

We used a Myo Armband at forearm to recognize the hand gestures that control the device motions. This device has electrically safe setup with low voltage battery and Bluetooth LE protocol, eight surface EMG sensors working at frequency of 2200 Hz and 9-DoF IMU working at 50 Hz. The provided software development kit (SDK) is suitable for working with the recorded data and for developing standalone applications. EMG signals are filtered through notch filters at frequencies of 50 and 60 Hz in order to take out any power-line interference. For the sake of simplicity, we considered the five gestures available with the SDK. These gestures mainly involve flexion/extension of fingers and flexion/ extension of hand.

We implemented specific types of grasps for both kinds of users in order to make better suitable to use the robotic finger with healthy hand or paretic hand. In particular, in case of healthy hand, we defined *anatomically impossible* grasps and ulnar grasps (see **Figures 6A,B**). In case of *anatomically impossible* grasp, the supernumerary robotic finger coordinates with human hand to grasp big size objects that cannot be grasped using only one hand. In *ulnar* grasp configuration, the robotic device coordinates with ring and pinkie fingers to grasp and hold an object, while the upper part of the hand (thumb, index, and medium fingers) is left free to do another task allowing, for instance, to hold multiple object in one hand or to unscrew a bottle cap with a single hand. In case of paretic hand users, we defined *power* and *precision* grasp as shown in **Figures 6C,D**. In the former, each module flexes with a fixed step size in order to wrap the finger around the object. In the latter, the target is to hold small size objects between the paretic limb and the device fingertip pad. To this aim, the fingertip is kept parallel to the paretic limb during flexion motion. The contact is expected to occur between the object and the fingertip module. Finally, the supernumerary robotic finger can actively be wrapped around the wrist as a bracelet when not used (see **Figure 6E**). We implemented a trigger-based FSM to control the motion of the robotic device (see **Figure 7B**). All the gestures were associated with a unique trigger signal. In **Figure 7A**, the gestures recognized through the Myo Armband are shown. In particular, *fist*-(event *e*1) switches the device from bracelet position to working position and vice versa. *Double tap*-(event *e*2) changes the grasp modalities. Patients with paretic hand can switch between precision and

*power* and *precision grasp*.

power grasp. When augmentation purpose is concerned, the user can switch between ulnar and anatomically impossible grasp. *Wave out*-(event *e*3) corresponds to flexion, and *Wave in*-(event *e*4) is associated with extension. Finally, *Finger spread*-(event *e*5) can stop the motion of the robotic finger.

## 3. EXPERIMENTS

In Section 2, we introduced a novel EMG interface to control motion and compliance of a supernumerary robotic finger. In the following, we demonstrate how this interface and the wearable device can be effectively used both to compensate paretic hand functions and to augment healthy human hand capabilities. We performed a proof-of-concept study involving four healthy subjects (three male and one female, aged 29–40 years). Written informed consent was obtained from the participants. The procedures were in accordance with the Declaration of Helsinki. The aim of this study was to verify the potential of the approach and to understand how rapidly the subjects can successfully interact with the wearable device by using the proposed EMG control interface. The experiments were divided into two categories. The first set of experiments was related to compensation of grasping function, whereas the second was related to augmentation of hand capabilities. In particular, the compensation experiments, shown in Section 3.1, have been carried out asking to the subjects to simulate a paretic hand. We focused mainly on bimanual tasks of activity of daily living (ADL). The augmentation experiments shown in Section 3.2 were performed with the healthy hand to show the effectiveness of the device in increasing the hand grasping abilities and workspace, e.g., allowing to grasp big size objects which can not be grasped using a single hand or holding multiple objects using the augmented hand, i.e., human hand and the supernumerary robotic finger. In both the experimental sets, the subjects used the EMG interface on one arm (three subject used the right arm, one the left), whereas the supernumerary robotic finger was worn on the other arm. The Myo Armband was positioned at the forearm, while the one channel electrodes interface on the biceps (see **Figure 2**).

### 3.1. Compensation of Paretic Hand Functions

Among the different ADL, we focused on those involving "hold and manipulate" tasks. Such activities are generally bimanual tasks where one hand is used to restrain the motion of one object, while the other operates on it, e.g., unscrew the cap of a bottle, open a beans can, etc. The proposed supernumerary finger can be an effective aid in such tasks (Hussain et al., 2016). To demonstrate how the EMG interface can be used by patients, we asked to the subjects to execute different ADL involving a hold and manipulate task (see **Figure 8**). In particular, the subjects were asked to grasp an object using the gestures of the hand and to regulate the grasp tightness acting on the stiffness of the device. We used a subset of objects from the YCB grasping toolkit (Çalli et al., 2015). This toolkit is intended to be used to facilitate benchmarking in prosthetic design, rehabilitation research, and robotic manipulation. The objects in the set are designed to cover

a wide range of aspects of the manipulation problem. It includes objects of daily life with different shapes, sizes, textures, weight, and rigidity. We considered six objects with different shapes to show how the robotic finger can adapt to the shape of the objects to realize a stable grasp. We mainly targeted the objects used in kitchen and in other ADL. During all the tests, subjects simulated the paretic hand and device was positioned on the arm as supposed to be used with the patients.

The subject was asked to perform different bimanual ADL without using the hand grasping ability where the device was worn. The controlateral arm was always used to control the device motion and joints stiffness. **Figure 8** shows the ADL tasks performed by simulating a paretic hand. All the targeted tasks normally require two healthy hands but have been successfully executed with the aid of the robotic extra finger even if one hand was non-functional. The robotic finger and paretic hand was used to constrain the object, while healthy hand was used for manipulation. **Figures 8A,B,D–F** show the example of opening the cans, box, and bottle with various shapes and different caps. **Figure 8C** reports the task of pouring water from a bottle while holding the glass with the help of robotic device and paretic arm. All the task were fulfilled controlling the device through the proposes' interface. The subjects used hand gestures to shape the finger around the object. Later, they controlled the grasp stiffness by contracting the controlateral arm biceps. Note that all the "opening" tasks required stiffness control to be executed. In fact, while compliant joints are preferable to adapt the shape of the finger to the object to grasp, a stiff device is necessary to achieve the stable grasps necessary while unscrewing the caps.

### 3.2. Augmenting Healthy Hand Function through the Proposed System

In this experiment, the subjects were asked to grasp a set of objects with the augmented hand to prove the effectiveness of the extra-robotic finger in enlarging the human hand workspace

that are impossible to do with human hand only. (A) Unscrewing a cap of bottle, (B) grasping bigger box, (C) grasping two balls, (D) opening door,

(E) soldering a board, and (F) grasping plate and glass.

and dexterity. We targeted tasks involving either *anatomically impossible* grasp or *ulnar* grasp, as defined in Section 2. In the former case, the subjects were asked to grasp relatively big size objects which cannot be grasped using only one hand. In the latter case, the users tried to grasp objects only using the ring and the pinkie fingers opposite to the sixth finger and to perform another operation with the remaining fingers (thumb, index, and middle). In **Figure 9A**, the user is unscrewing a cap from a bottle using only one hand. Ulnar grasp is used to keep firm the bottle, while the other fingers can unscrew the cap. **Figure 9B** shows the example of grasping big size box with the augmented hand that is impossible to grasp with the human hand only. Holding multiple objects with the augmented hand is shown in **Figures 9C,F**. The example illustrated in **Figure 9D** involves the task of opening the door using the handle, while carrying a heavy bag with the hand. The user was able to turn the handle to open the door using the robotic device, while keep holding the bag with the hand. The 9-e is another example where the user can solder a circuit board, while holding the board by robotic finger, ring, and pinkie. The thumb, index, and middle finger are used to hold soldering gun. Note that, all the tasks are either impossible or at least very difficult to be carrying out with a single hand. All these tasks were successfully fulfilled by all the subjects with the help of the EMG interface and the supernumerary extra finger. Also in this subset of examples, the possibility to control both motion and joint stiffness of the device was exploited by the users.

### 4. RESULTS

In Section 3, we described the tasks performed by the subjects to prove the usability of the proposed EMG interface and the novel supernumerary finger prototype. In the following, we will give the details of the position of the device and the forces exerted on the grasped object for two particular type of grasps, i.e., power and precision grasps. **Figures 10**–**13** reported the behavior of the device during power and precision grasping, respectively. In particular, **Figures 10** and **11** refer to the power grasp reported in **Figure 8A**, whereas **Figures 12** and **13** refer to the precision grasp reported in **Figure 9E**. We report only these examples for the sake of brevity. **Figures 10**–**13** represent the average of five repetitions of the same subject. To measure the forces exerted on the objects, we equipped each module of the extra finger with a Force Sensing Resistor (FSR) (408, Interlink Electronics Inc., USA). The user was asked to command the supernumerary finger till the grasp is obtained. Once the device was in contact with the object, the user increased the stiffness of the device by cocontracting his/her biceps (see **Figure 14**). The contraction of the biceps was read by the EMG interface, and the value of *kd* in equation (1) was increased (see **Figure 15**). This variation produced a variation in the desired angle *qdes* of the modules, while the read actual position of the modules remained the same due to the constrain imposed by the object (see **Figures 11** and **13**). The variation in the desired angles produces, however, an increase of the force exerted by the device onto the object, as shown in **Figures 10** and **12**. So that, by cocontracting the biceps the user can regulate the grasp tightness. As expected, in power grasps, all the modules move of a similar angle so as to wrap the object. All the modules also contribute to the grasp tightness applying force on the object. Differently, in precision grasp, the fingertip module is the only module exerting force. The module motion is opposed to the direction of the other three modules so as to leave the fingertip parallel to the hand.

After the experiments, we investigated the users' subjective satisfaction and possible concerns related to the proposed system. We proposed a questionnaire to the subjects to evaluate their satisfaction and usefulness of the proposed system. Questionnaires and interviews are recommended methods for user feedback and what features they particularly like or dislike in the system (Nielsen, 1994). The subjects were asked to fill the Usefulness Satisfaction and Ease of use questionnaire (USE) (Lund, 2001) that focuses on the experience of the system usage. This questionnaire uses a seven-point Likert rating scale. Mean and SD of the questionnaire factors are presented in **Table 3**.

The proposed EMG interface and the novel robotic extra finger prototype successfully enabled the users to complete all the targeted tasks both related to augmentation and compensation. The experiments proved that the presented system can be an effective aid both in augmenting the healthy human hand and in compensating its missing abilities in case of a disease. The proposed EMG control interface resulted to be intuitive and simple. The users were able to generate multiple control inputs without using sensorized gloves on human hand and were able to modulate the compliance of the robotic device in proportional to the EMG signal amplitude variations in biceps. Moreover, the upgraded version of the device with additional adduction/abduction degree of freedom increased the dexterity of the robotic device allowing more complex operation, especially when hand augmentation was considered.

### 5. DISCUSSION

Supernumerary robotic limbs are a new generation of wearable robots which aims at assisting natural limbs by closely interacting with them. In order to realize safe and natural interaction of human limbs with the extra-robotic limbs, the control principles, actuation, and sensing capabilities of extra limbs should have similar behavior as humans ones, e.g., their ability to regulate compliance. In this regard and to overcome the limitations of the control interfaces presented in state of the art for supernumerary robotic fingers, we propose a novel EMG interface. In particular, to obtain multiple user control inputs to control the motion of extra-robotic finger, as well as to regulate its compliance, we have

presented an EMG-based control interface that can be used to control different trajectories for finger flexion/extension and can regulate the finger compliance and thus the tightness of the grasp. The exploitation of the supernumerary robotic fingers in compensating and augmenting the human hand grasping abilities is at an early stage. One of the major challenges in augmenting/compensating human capabilities through robotic extra limbs concerns the development of a suitable control interfaces for the integration of the device motion with that of the human. We better demonstrate this fact by recalling the approaches presented in literature and their limitations. Wu and Asada (2014) presented a control algorithm enabling a human

#### TABLE 3 | Questionnaire factors and relative marks.

*The mark ranges from "1* = *strongly disagree" to "7* = *strongly agree." Mean and SD are reported.*