EMG feedback outperforms force feedback in the presence of prosthesis control disturbance

Abstract

Closing the prosthesis control loop by providing artificial somatosensory feedback can improve utility and user experience. Additionally, closed-loop control should be more robust with respect to disturbance, but this might depend on the type of feedback provided. Thus, the present study investigates and compares the performance of EMG and force feedback in the presence of control disturbances. Twenty able-bodied subjects and one transradial amputee performed delicate and power grasps with a prosthesis in a functional task, while the control signal gain was temporarily increased (high-gain disturbance) or decreased (low-gain disturbance) without their knowledge. Three outcome measures were considered: the percentage of trials successful in the first attempt (reaction to disturbance), the average number of attempts in trials where the wrong force was initially applied (adaptation to disturbance), and the average completion time of the last attempt in every trial. EMG feedback was shown to offer significantly better performance compared to force feedback during power grasping in terms of reaction to disturbance and completion time. During power grasping with high-gain disturbance, the median first-attempt success rate was significantly higher with EMG feedback (73.3%) compared to that achieved with force feedback (60%). Moreover, the median completion time for power grasps with low-gain disturbance was significantly longer with force feedback than with EMG feedback (3.64 against 2.48 s, an increase of 32%). Contrary to our expectations, there was no significant difference between feedback types with regards to adaptation to disturbances and the two feedback types performed similarly in delicate grasps. The results indicated that EMG feedback displayed better performance than force feedback in the presence of control disturbances, further demonstrating the potential of this approach to provide a reliable prosthesis-user interaction.

Wrist speed feedback improves elbow compensation and reaching accuracy for myoelectric transradial prosthesis users in hybrid virtual reaching task

“…These tasks require coordination of elbow angle and wrist EMG to complete the reach. This paradigm differed from other similar studies into proprioceptive feedback by using a ballistic reach paradigm, which prevented subjects from incorporating feedback into their task performance and allowed us to investigate solely the impact of feedback on improvements to feedforward control [ 10 , 13 , 41 , 42 ]. Our results provide some insight into how artificial joint speed feedback may be used to improve control of a myoelectric prosthesis.…”

“…Consequently, artificial sensory feedback has received much attention over the past decade [ 3 , 4 ]. Typically, this takes the form of sensory substitution feedback, where the information provided from missing sensory organs is communicated to the user via an alternative method such as vibrotactile [ 5 – 10 ] or auditory stimuli [ 11 – 13 ], or via direct nerve stimulation [ 14 – 16 ].…”

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

“…The proportional myoelectric control was robust and reliable despite the tight fit on the forearm and the fact that the subjects moved the prosthesis to reach and grasp the target object. Now, when the technical platform has been developed and assessed, and the anatomically congruent encoding (coupled scheme) verified as effective, we can proceed and evaluate the impact of closedloop control on the sensorimotor integration aspects during prolonged and/or more challenging prosthesis use (e.g., introducing disturbance [43], intermittent feedback). Indeed, we assume that the true benefits of anatomically congruent feedback would be expressed in these paradigms, when the subjects have enough time to integrate the feedback into the internal representation of the motors task and develop internal models of prosthesis behavior [14].…”

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

“…In this study, we focused on an EMG feedback-based interface that has previously been shown to outperform other non-invasive feedback approaches to prosthesis force control [25,32]. We elicited a wide range of success rates by enforcing task execution at different speeds using a time-band methodology, similar to previous studies that investigated motor control of natural movements [16,22].…”