Joint speed feedback improves myoelectric prosthesis adaptation after perturbed reaches in non amputees

Abstract

Accurate control of human limbs involves both feedforward and feedback signals. For prosthetic arms, feedforward control is commonly accomplished by recording myoelectric signals from the residual limb to predict the user’s intent, but augmented feedback signals are not explicitly provided in commercial devices. Previous studies have demonstrated inconsistent results when artificial feedback was provided in the presence of vision; some studies showed benefits, while others did not. We hypothesized that negligible benefits in past studies may have been due to artificial feedback with low precision compared to vision, which results in heavy reliance on vision during reaching tasks. Furthermore, we anticipated more reliable benefits from artificial feedback when providing information that vision estimates with high uncertainty (e.g. joint speed). In this study, we test an artificial sensory feedback system providing joint speed information and how it impacts performance and adaptation during a hybrid positional-and-myoelectric ballistic reaching task. We found that overall reaching errors were reduced after perturbed control, but did not significantly improve steady-state reaches. Furthermore, we found that feedback about the joint speed of the myoelectric prosthesis control improved the adaptation rate of biological limb movements, which may have resulted from high prosthesis control noise and strategic overreaching with the positional control and underreaching with the myoelectric control. These results provide insights into the relevant factors influencing the improvements conferred by artificial sensory feedback.

Wrist Speed Feedback Improves Elbow Compensation and Reaching Accuracy for Myoelectric Transradial Prosthesis Users in Hybrid Virtual Reaching Task

“…Additionally, the same compensatory behavior was observed in both studies, where subjects would strategically underreach with the wrist and compensate by overreaching with the elbow to minimize the distance to the target. Interestingly, while we showed no impact of sensory feedback on the average errors in the previous study 27 , amputee reaches in the present study demonstrated lower elbow and wrist biases with feedback available [Fig. 4a].…”

“…In our previous research, we explicitly considered the uncertainty of incidental visual feedback and provided non-amputee subjects with joint speed feedback, which signi cantly reduced the uncertainty of joint speed within a moving reference frame 26 . In a following study, we found that joint speed feedback reduced reaching errors after a perturbation to a 1 degree-of-freedom (DoF) myoelectric controller 27 , however these studies could not fully investigate the context of a person with amputation using myoelectric control. Proprioceptive organs including muscle spindles and Golgi tendon organs are activated differently in an amputated limb than they are in intact limb; agonist-antagonist muscles pairs stimulate these organs during movement 28 , but this pairing is generally absent from amputated limbs.…”

“…The purpose of this study was to extend the methods of our previous research 27 with tests with amputee subjects to investigate the effect of joint speed feedback on prosthesis control and adaptation to errors during reaching. Transradial amputee subjects controlled a virtual 1-DoF myoelectric limb and completed center-out reaching tasks under steady-state and perturbed dynamics conditions.…”

Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user

“…Second, a linear change in speed, as controlled by the ALC, may not relate to a linear increase in the speed of the prosthesis, making these differences in control signals further difficult to discern. It is possible that providing sensory feedback of the speed calculated from the control signals may help the pilot to better identify his prosthesis speed, or to adapt to changing conditions [ 25 ].…”

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

“…In the present study, the control disturbance was implemented by multiplying the prosthesis control signal by a gain, unbeknownst to the subjects ( Figure 2 , “disturbance gain selection” block) ( Cipriani et al, 2014 ). Similarly to an approach implemented in Earley et al (2017) and Earley et al (2021) , the gain of the myoelectric signal was doubled or reduced by 33% at the beginning of a disturbed trial. When the gain was doubled (high-gain disturbance), the system became more sensitive with respect to the nominal condition and the same muscle contraction now generated a stronger myoelectric signal.…”