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

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.DFG, 325093850, Open Access Publizieren 2017 - 2018 / Technische Universität Berli

Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users

Myoelectric hand prostheses are usually controlled with two bipolar electrodes located on the flexor and extensor muscles of the residual limb. With clinically established techniques, only one function can be controlled at a time. This is cumbersome and limits the benefit of additional functions offered by modern prostheses. Extensive research has been conducted on more advanced control techniques, but the clinical impact has been limited, mainly due to the lack of reliability in real-world conditions. We implemented a regression-based control approach that allows for simultaneous and proportional control of two degrees of freedom and evaluated it on five prosthetic end users. In the evaluation of tasks mimicking daily life activities, we included factors that limit reliability, such as tests in different arm positions and on different days. The regression approach was robust over multiple days and only slightly affected by changing in the arm position. Additionally, the regression approach outperformed two clinical control approaches in most conditions

Example of cursor traces travelled during the first two evaluation runs (a) before (run 10 and 11) and (b) after donning/doffing (run 22 and 23), which are derived from the same subject.

<p>Different colored circles and traces indicate randomly presented 16 targets and the paths travelled to achieve the corresponding targets, respectively. The numbers in each target represent the sequence of target appearance. The solid and dashed circles mean successfully achieved and missed targets, respectively, and the small black rectangles represent overshoot. The mean path efficiencies of run 10, 11, 22, and 23 are 40.40, 62.16, 40.75, and 46.29%, respectively.</p

Online performance estimated (a) before and (b) after donning/doffing for all metrics, and (c) statistical test results showing the significant difference (gray colored entry, <i>p</i> < 0.05) between the performance obtained before and after donning/doffing at the corresponding combinations of training and test positions.

<p>The error bars in (a) and (b) represent the standard deviations of the performance values of the thirteen normally limbed subjects. No significant performance loss is observed even when test arm positions are different from training positions, compared to the performance obtained from the same training and test positions (a) before donning/doffing. Only two cases show significant performance differences between test arm positions (P1 and P2 training positions for user effort before donning/doffing). When comparing the online performance in terms of the training position, there is only one case showing statistically better performance than the others (P2 > P1 for path efficiency before donning/doffing). A similar trend is also observed (b) after donning/doffing even if overall online performance decreases, compared to before donning/doffing. (c) Statistically significant performance loss is frequently observed between before and after donning/doffing, except one performance metric (stopping path).</p

Correlation matrices showing the significant correlation (colored element) between the offline and online performance at the corresponding combinations of training and test positions.

<p>Diagonal and off-diagonal elements represent the correlation results when no impact and arm position change impact are introduced, respectively. When training and test arm positions are the same (no control condition change), there is no significant correlation between the offline and online control performance (see the diagonal entries of the correlation matrices). Only three of forty-two cases show statistically significant correlation between the offline and online performance when the impact of arm position change is introduced (see the off-diagonal entries of the correlation matrices). The correlation analysis results for the ‘Com’ training position were not considered in counting the number of the significant cases, because the ‘Com’ model was trained by combining the training data of all training positions.</p

Forearm of a normally limbed subject wearing the textile hose including 16 electrodes.

<p>Forearm of a normally limbed subject wearing the textile hose including 16 electrodes.</p

Results of the subject with congenital upper-limb deficiency.

<p>Online Performance estimated (a) before and (b) after donning/doffing for all metrics, and (c) comparison of the mean performance estimated before and after donning/doffing (*<i>p</i> < 0.05 and ***<i>p</i> < 0.001). The error bars represent the standard deviations of the performance values. Although the overall online performance decreases after donning/doffing as the results of the able-bodied subjects, the original performance obtained before donning/doffing is fairly retained even after donning/doffing. Unlike the online results of the able-bodied subjects, significant decrease in online performance is shown in only two performance metrics, throughput and user effort, after donning/doffing. Also, two other performance metrics, overshoot and path efficiency, even show slightly better performance after donning/doffing in average.</p

Offline intra- and inter-position performance of the subject with congenital deficiency.

<p>The red, green and blue bards show the R² values estimated from three test arm positions (P1, P2, and P3), respectively, for the four training positions (P1, P2, P3, and Com). The fundamental trend shown in the able-bodied subjects’ result (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186318#pone.0186318.g005" target="_blank">Fig 5</a>) is similarly observed, in that intra-position R² values are generally higher than inter-position ones. Compared to the offline result of the intact-limb subjects (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0186318#pone.0186318.g005" target="_blank">Fig 5</a>), there are two distinct points: 1) P2 test position performs good for all training positions and 2) P3 test position is more severely influenced by the training position (see the performance of P3 test position at P2 and ‘Com’ training positions).</p

Description of seven performance metrics.

<p>Description of seven performance metrics.</p

Mean intra- and inter-position offline performance of the thirteen able-bodied subjects.

<p>The red, green, and blue bars show the mean R² values estimated from three test arm positions (P1, P2, and P3), respectively, for the corresponding training positions (P1, P2, P3, and Com). The mean intra-position R² values are significantly higher than the inter-position ones at all training positions, and there are statistical differences between them for most cases (<i>p</i> < 0.05). Good offline performance is attained from all test positions at the ‘Com’ training position and there is no considerable performance difference between test positions. The error bars represent the standard deviations of the mean R² values.</p