Building an internal model of a myoelectric prosthesis via closed-loop control for consistent and routine grasping

Abstract

Prosthesis users usually agree that myoelectric prostheses should be equipped with somatosensory feedback. However, the exact role of feedback and potential benefits are still elusive. The current study investigates the nature of human control processes within a specific context of routine grasping. Although the latter includes a fast feedforward control of the grasping force, the assumption was that the feedback would still be useful; it would communicate the outcome of the grasping trial, which the subjects could use to learn an internal model of feedforward control. Nine able-bodied subjects produced repeatedly a desired level of grasping force using different control configurations: feedback versus no-feedback, virtual versus real prosthetic hand, and joystick versus myocontrol. The outcome measures were the median and dispersion of the relative force errors. The results demonstrated that the feedback was successful in limiting the variability of the routine grasping due to uncertainties in the system and/or the command interface. The internal models of feedforward control could be employed by the subjects to control the prosthesis without the loss of performance even after the force feedback was removed. The models were, however, unstable over time, especially with myocontrol. Overall, the study demonstrates that the prosthesis system can be learned by the subjects using feedback. The feedback is also essential to maintain the model, and it could be delivered intermittently. This approach has practical advantages, but the level to which this mechanism can be truly exploited in practice depends directly on the consistency of the prosthesis control interface.

Multichannel Electrotactile Feedback With Spatial and Mixed Coding for Closed-Loop Control of Grasping Force in Hand Prostheses

“…In real life, the users usually look at the prosthesis while grasping, and as demonstrated before [33], the closing velocity is an important input that can assist the subjects in force control across routine grasping trials. The fact that electrotactile feedback resulted in the similar performance as the benchmark visual feedback can be attributed to the uncertainty of the feedforward pathway [33], [34]. The myocontrol based on surface EMG signals is an inherently noisy command interface.…”

“…Furthermore, the humans are capable of sensor data fusion, state estimation and optimal and predictive control [38], [39], and these abilities are likely to fundamentally impact the performance of prosthetic control as well. The fact that basic principles of human motor control needs to be considered when designing closed-loop systems in prosthetics has been acknowledged in literature and the research is in progress [26], [34], [36].…”

“…This conclusion was confirmed by the fact that the performance across the feedback conditions remained similar also when using the simulated hand. Previous studies investigating the accuracy and precision of the prosthesis force control also recognized the impact of the feedforward channel on the closed-loop [33], [34]. The higher target force increased the dispersion and decreased the accuracy during repeated grasping and also affected the effectiveness of feedback [33].…”

“…The higher target force increased the dispersion and decreased the accuracy during repeated grasping and also affected the effectiveness of feedback [33]. Furthermore, the reliability of the feedforward interface (EMG vs. joystick) impacted on the quality of feedback-driven learning [34]. Finally, the difficulties in control were pointed out in [22] as one possible reason why vibrotactile feedback failed to improve the performance in naïve users.…”

“…The routine grasping task evaluated the subject ability to grasp objects in a straightforward manner, by increasing and then holding the contraction level (feedforward command) so that the grasping force upon initial contact with the object was at the desired force level [33], [34]. The reference forces were set to 30, 50 and 70% of prosthesis maximum and for each level the subjects performed 70 trials.…”