Resolving the effect of wrist position on myoelectric pattern recognition control

Adewuyi, Adenike A.; Hargrove, Levi J.; Kuiken, Todd A.

doi:10.1186/s12984-017-0246-x

Cited by 25 publications

(27 citation statements)

References 24 publications

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“…Although our control system has shown a high robustness against changing arm position when tested online, other posture related factors may still impact the performance. According to recent studies [ 60 – 62 ], changing wrist positions can significantly deteriorate both offline and online myoelectric control performance for able-bodied subjects and partial-hand amputees. Therefore, further work should be conducted to investigate related factors in more detail and for other user groups such as trans-carpal amputees.…”

Section: Discussionmentioning

confidence: 99%

Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

Hwang

Hahne²,

Müller

2017

PLoS ONE

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There are some practical factors, such as arm position change and donning/doffing, which prevent robust myoelectric control. The objective of this study is to precisely characterize the impacts of the two representative factors on myoelectric controllability in practical control situations, thereby providing useful references that can be potentially used to find better solutions for clinically reliable myoelectric control. To this end, a real-time target acquisition task was performed by fourteen subjects including one individual with congenital upper-limb deficiency, where the impacts of arm position change, donning/doffing and a combination of both factors on control performance was systematically evaluated. The changes in online performance were examined with seven different performance metrics to comprehensively evaluate various aspects of myoelectric controllability. As a result, arm position change significantly affects offline prediction accuracy, but not online control performance due to real-time feedback, thereby showing no significant correlation between offline and online performance. Donning/doffing was still problematic in online control conditions. It was further observed that no benefit was attained when using a control model trained with multiple position data in terms of arm position change, and the degree of electrode shift caused by donning/doffing was not severely associated with the degree of performance loss under practical conditions (around 1 cm electrode shift). Since this study is the first to concurrently investigate the impacts of arm position change and donning/doffing in practical myoelectric control situations, all findings of this study provide new insights into robust myoelectric control with respect to arm position change and donning/doffing.

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Section: Discussionmentioning

confidence: 99%

Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

Hwang

Hahne²,

Müller

2017

PLoS ONE

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“…Static forearm orientation experiments have been conducted by many researchers [23,[119][120][121][122][123][124][125]. Within their protocols, shoulder and elbow joint angles are typically held constant throughout all collections.…”

Section: Static Forearm Orientationmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

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This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

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“…In addition, many studies of PR control have focused on engineering approaches (such as study of data sources, features, and classifiers) in order to increase EMG PR accuracy and reliability in identifying user intent [ 26 – 29 ], while very limited research has been conducted to quantify the efficacy of EMG PR-based prosthesis control as compared to clinically available direct myoelectric control. Therefore, the answers to clinically relevant questions have not been addressed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control

Resnik

Huang

Winslow

et al. 2018

J NeuroEngineering Rehabil

132

139

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BackgroundAlthough electromyogram (EMG) pattern recognition (PR) for multifunctional upper limb prosthesis control has been reported for decades, the clinical benefits have rarely been examined. The study purposes were to: 1) compare self-report and performance outcomes of a transradial amputee immediately after training and one week after training of direct myoelectric control and EMG pattern recognition (PR) for a two-degree-of-freedom (DOF) prosthesis, and 2) examine the change in outcomes one week after pattern recognition training and the rate of skill acquisition in two subjects with transradial amputations.MethodsIn this cross-over study, participants were randomized to receive either PR control or direct control (DC) training of a 2 DOF myoelectric prosthesis first. Participants were 2 persons with traumatic transradial (TR) amputations who were 1 DOF myoelectric users. Outcomes, including measures of dexterity with and without cognitive load, activity performance, self-reported function, and prosthetic satisfaction were administered immediately and 1 week after training. Speed of skill acquisition was assessed hourly. One subject completed training under both PR control and DC conditions. Both subjects completed PR training and testing. Outcomes of test metrics were analyzed descriptively.ResultsComparison of the two control strategies in one subject who completed training in both conditions showed better scores in 2 (18%) dexterity measures, 1 (50%) dexterity measure with cognitive load, and 1 (50%) self-report functional measure using DC, as compared to PR. Scores of all other metrics were comparable. Both subjects showed decline in dexterity after training. Findings related to rate of skill acquisition varied considerably by subject.ConclusionsOutcomes of PR and DC for operating a 2-DOF prosthesis in a single subject cross-over study were similar for 74% of metrics, and favored DC in 26% of metrics. The two subjects who completed PR training showed decline in dexterity one week after training ended. Findings related to rate of skill acquisition varied considerably by subject. This study, despite its small sample size, highlights a need for additional research quantifying the functional and clinical benefits of PR control for upper limb prostheses.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0361-3) contains supplementary material, which is available to authorized users.

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Resolving the effect of wrist position on myoelectric pattern recognition control

Cited by 25 publications

References 24 publications

Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Evaluation of EMG pattern recognition for upper limb prosthesis control: a case study in comparison with direct myoelectric control

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