Reducing Motor Variability Enhances Myoelectric Control Robustness Across Limb Positions

Abstract

Background: The limb position effect is a multi-faceted problem, associated with decreased upper-limb prosthesis control acuity following a change in arm position. The many factors contributing to this problem can arise from distinct environmental or physiological sources. Despite their differences in origin, the effect of each factor manifests similarly as increased variability in the detected control signal. This variability can cause incorrect decoding of user intent, leading to dropped items or inability to use the prosthesis during activities of daily living. The general approach of previous research has attempted to limit the impact of the factors or better capture the variability with data abundance. In this paper we take an alternative approach and investigate the effect of reducing the variability of the control signal by improving the consistency of muscle activity with user training. Methods: Participants underwent 4 days of myoelectric training with either concurrent or delayed feedback in a single arm position. During this time, they were trained to control a two-dimensional cursor using muscles in the forearm. At the end of training participants underwent a zero feedback retention test in multiple limb positions. In doing so, we tested how well the skill learned in a single limb position generalized to untrained positions. Results: We found that delayed feedback training led to more consistent muscle activity across both the trained and untrained limb positions. Analysis of patterns of activation in the delayed feedback group suggest a structured change in muscle activity occurs across arm positions. The structured changes allowed us to quantify the limb position effect by comparing trained to untrained arm positions. Different limb positions changed mean ECR and FCR muscle activity in the range of -4.3% to +18.7%. All participants were able to counter the limb position effect if given concurrent feedback, confirming our results align with existing findings. Conclusions: Our results demonstrate that myoelectric user-training can lead to the retention of motor skills that are more robust to limb position changes. This work highlights the importance of reducing motor variability with practice, prior to examining the underlying structure of muscle changes associated with limb position. These findings will be useful for the majority of myoelectric prosthesis control systems and will create better quality input data leading to more robust machine-learning based prosthesis control systems.

Biomimetic versus arbitrary motor control strategies for bionic hand skill learning

“…Externally, consider that the myoelectric interface-between the surface EMG electrodes and the residual limb-is highly inconsistent throughout the day 44 due to issues pertaining to socket fit, which can often be exacerbated by sweat build-up and/or rain or humidity. Internally, changes in arm posture during muscle contraction have been shown to impact muscle geometry in different ways (muscle fibre length, diameter and orientation), which in turn can systematically alter the EMG features for a specific muscle 45,46 . Consequently, a sensor can receive highly variable signals for the same intended contraction.…”

Does Exerting Grasps Involve a Finite Set of Muscle Patterns? A Study of Intra- and Intersubject Variability of Forearm sEMG Signals in Seven Grasp Types

“…This redundancy means that the neuromotor system can select different combinations of muscular activities to perform the same grasp and these combinations could differ among subjects, and even among the trials done by the same subject [30]. Inherent motor abundance means there are multiple solutions for the same task goal [31]. Consequently, the specific role of muscles for performing ADL is still poorly understood.…”

“…Furthermore, identical movements may have slightly different representations in the muscle domain, which can cause incorrect decoding of user intent and negatively affect the robustness of sEMG devices. A recent work suggests that reducing motor variability could enhance the myolelectric control robustness of robotic devices [31].…”