Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies

Abstract

Ongoing developments in myoelectric prosthesis control have provided prosthesis users with an assortment of control strategies that vary in reliability and performance. Many studies have focused on improving performance by providing feedback to the user, but have overlooked the effect of this feedback on internal model development, which is key to improving long-term performance. In this work, the strength of internal models developed for two commonly used myoelectric control strategies: raw control with raw feedback (using a regression-based approach), and filtered control with filtered feedback (using a classifier-based approach), were evaluated using two psychometric measures: trial-by-trial adaptation and just-noticeable-difference. The performance of both strategies was also evaluated using a Schmidt’s style target acquisition task. Results obtained from 24 able-bodied subjects showed that although filtered control with filtered feedback had better short-term performance in path efficiency (p < 0.05), raw control with raw feedback resulted in stronger internal model development (p < 0.05), which may lead to better long-term performance. Despite inherent noise in the control signals of the regression controller, these findings suggest that rich feedback associated with regression control may be used to improve human understanding of the myoelectric control system.

Neural feedback strategies to improve grasping coordination in neuromusculoskeletal prostheses

“…In unimpaired subjects, the internal model is constantly updated using somatosensory information provided by intact biological sensors. Users of myoelectric prostheses deprived of somatosensory feedback cannot properly update such internal models, resulting in a poorer control experience 32,33 . Existing literature in this field suggests that open-loop control might be sufficient to retain internal models [34][35][36][37][38] .…”

Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator

“…The controller gain in the mouse cursor study is equivalent to the mouse sensitivity. In the catch-trial perturbation data we analyzed [25,38], the controller gain is equivalent to the EMG control gain.…”

“…We compared these values to the results obtained from the same trial sets using a perturbation study regression comparing error versus perturbation level [2]. In the simulation data (Fig 2A), experiment length (80 trials) and perturbation frequency (one perturbation occurring on the 6 th , 7 th or 8 th trial of every 8-trial block) were set to match those of the empirical data (Fig 2B) [25,38]. The simulation perturbation level was set at 50% of the movement distance implemented as a shift in the simulated movement’s endpoint position at Eq 1.…”

“…Empirical data displayed in Figs 1 and 2B are from Shehata et al [25] and are available here: https://doi.org/10.5061/dryad.v12f25n [38]. Data in S1 Fig are from Ikegami et al [30].…”

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

“…classifier-based pattern recognition (PR) and regression, have been extensively investigated in recent literature. Unlike PR-based methods which discriminate hand gestures in a discrete and sequential manner [3], regression models focus on continuous wrist kinematics estimation [4] and thus can promote simultaneous and proportional control in multiple degrees of freedoms (DoF). Several MLbased regression methods, including linear regression (LR), artificial neural network (ANN), kernel ridge regression, support vector regression (SVR) and random forest (RF), have been extensively exploited in both off-line simulations [5][6][7][8][9] and real-time prosthetic control [1].…”