Estimating speed-accuracy trade-offs to evaluate and understand closed-loop prosthesis interfaces

Abstract

Objective: Closed-loop prosthesis interfaces, which combine electromyography (EMG)-based control with supplementary feedback, represent a promising direction for developing the next generation of bionic limbs. However, we still lack an understanding of how users utilize these interfaces and how to evaluate competing solutions. In this study, we used the framework of speed-accuracy trade-off functions (SAF) to understand, evaluate, and compare the performance of two closed-loop user-prosthesis interfaces. Approach: Ten able-bodied participants and an amputee performed a force-matching task in a functional box-and-block setup at three different speeds. All participants were subjected to both interfaces in a crossover study design with a one-week washout period. Importantly, both interfaces used (identical) direct proportional control but differed in the feedback provided to the participant (EMG feedback vs. Force feedback). Therefore, we estimated the SAFs afforded by the two interfaces and sought to understand how the participants planned and executed the task under the various conditions. Main results: We found that execution speed significantly influenced performance, and that EMG feedback afforded better overall performance, especially at medium speeds. Notably, we found that there was a difference in the SAF between the two interfaces, with EMG feedback enabling participants to attain higher accuracies faster than Force feedback. Furthermore, both interfaces enabled participants to develop flexible control policies, while EMG feedback also afforded participants the ability to generate smoother, more repeatable EMG commands. Significance: Overall, the results indicate that the performance of closed-loop prosthesis interfaces depends critically on the feedback approach and execution speed. This study showed that the SAF framework could be used to reveal the differences between feedback approaches, which might not have been detected if the assessment was performed at a single speed. Therefore, we argue that it is important to consider the speed-accuracy trade-offs to rigorously evaluate and compare user-prosthesis interfaces.

Closed-Loop Control of a Multifunctional Myoelectric Prosthesis With Full-State Anatomically Congruent Electrotactile Feedback

“…However, the provision of feedback increased the time to perform the task, which is an effect that has been already demonstrated in the literature [31], [32], [41]. Therefore, introducing feedback entails a trade-off between improved accuracy and longer time [42], but the latter is likely to decrease following more extensive training. The main contribution of this work is the technical and perceptual feasibility, while short exposure to the feedback prevented assessing the sensorimotor integration.…”

Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs

“…The experimental paradigm follows from a previous experiment [22], which for the present study was extended across days. The methods are briefly described here.…”

“…The signals were subsequently filtered using a 2 nd -order Butterworth low-pass filter with a 0.5 Hz cutoff, and this 'myoelectric command' from each of the electrodes was normalized to 50% of that obtained during maximum voluntary contraction (MVC) (following the results of [28]). The normalized myoelectric commands obtained from the flexor EMG were translated into the normalized input command for the prosthesis using a piecewise linear mapping defined by the breakpoints given in table 1 (see [22]). Note that the EMG intervals were wider for higher amplitudes to compensate for the higher variability of the EMG at stronger contractions.…”

“…Apart from quantifying skill, SAFs have been used in a recent study to understand and compare how different feedback interfaces afford different speedaccuracy tradeoffs in prosthesis force control [22]. The study demonstrated the relevance of SAFs and showed how comparing the interfaces at a single speed (corresponding to a single point on the SAF profile), as commonly done in the literature, could generate misleading conclusions.…”

One-shot random forest model calibration for hand gesture decoding

“…Many myoelectric control systems use machine learning to map the electromyographic (EMG) signals [1][2][3][4][5] to control commands for human-machine interfaces, e.g. prosthesis [6][7][8][9][10][11][12] and virtual keyboards [13,14]. Most modern myoelectric control machine learning models require a large amount of data from a user to learn a bespoke and user-specific map [7,15,16].…”