EMG space similarity feedback promotes learning of expert-like muscle activation patterns in a complex motor skill

Abstract

Augmented feedback provided by a coach or augmented reality system can facilitate the acquisition of a motor skill. Verbal instructions and visual aids can be effective in providing feedback about the kinematics of the desired movements. However, many skills require mastering not only kinematic, but also complex kinetic patterns, for which feedback is harder to convey. Here, we propose the electromyography (EMG) space similarity feedback, which may indirectly convey kinematic and kinetic feedback by comparing the muscle activations of the learner and an expert in the task. The EMG space similarity feedback is a score that reflects how well a set of muscle synergies extracted from the expert can reconstruct the learner’s EMG when performing the task. We tested the EMG space similarity feedback in a virtual bimanual polishing task that uses a robotic system to simulate the dynamics of a real polishing operation. We measured the expert’s and learner’s EMG from eight muscles in each arm during the real and virtual polishing tasks, respectively. The goal of the virtual task was to smoothen the surface of a virtual object. Therefore, we defined performance in the task as the smoothness of the object at the end of a trial. We separated learners into real feedback and null feedback groups to assess the effects of the EMG space similarity feedback. The real and null feedback groups received veridic and no EMG space similarity feedback, respectively. Subjects participated in five training sessions on different days, and we evaluated their performance on each day. Subjects in both groups were able to increase smoothness throughout the training sessions, with no significant differences between groups. However, subjects in the real feedback group were able to improve in the EMG space similarity score to a significantly greater extent than the null feedback group. Additionally, subjects in the real feedback group produced muscle activations that became increasingly consistent with an important muscle synergy found in the expert. Our results indicate that the EMG space similarity feedback promotes acquiring expert-like muscle activation patterns, suggesting that it may assist in the acquisition of complex motor skills.

Bridging Human-Robot Co-Adaptation via Biofeedback for Continuous Myoelectric Control

“…This discovery has prompted researchers to reconsider that rather than relying solely on brute-force machine calculations to approach the limits of user intent recognition, could we instead adopt a more user-centered strategy by helping users adapt to machines? For this reason, Barradas et al [14] proposed the EMG space similarity feedback to improve the consistency of users' muscle synergy features. Fang et al [15] introduced a clustering feedback strategy that provides users with real-time bio-feedback by visualizing the online sEMG signal input and the centroids of the training samples.…”

“…Users are required to passively generate movements according to instructions, but cannot receive the feedback from the machine, thereby this phase is usually considered as an open-loop calibration, where without the feedback the user's adaptability is essentially excluded from the training set. In the testing phase, while human adaptability can be fully engaged by multiple biofeedback methods [14], [15], [20], it only makes people more adaptable to the trained model, the myoelectric control model itself does not integrate adaptability. As a result, machine intelligence and user adaptability are isolated in the training and testing phases respectively.…”