Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning

TL;DR

This paper presents a novel high-density sEMG prosthetic control system with incremental learning, enabling accurate long-term control of 7 motions and addressing challenges like distribution shift and sensor limitations.

Contribution

Introduction of a compact HD-sEMG interface with 64 electrodes and an incremental learning system for long-term, personalized prosthetic control, along with the release of a comprehensive dataset.

Findings

Effective control of 7 motions over several months.

Incremental learning improves model adaptation over time.

High-density sEMG enhances accuracy and controllability.

Abstract

Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized controllers from user data. While promising, they often suffer from distribution shift during long-term usage, requiring costly model re-training. Moreover, most prosthetic sEMG sensors have low spatial density, which limits accuracy and the number of controllable motions. In this work, we address both challenges by introducing a novel myoelectric prosthetic system integrating a high density-sEMG (HD-sEMG) setup and incremental learning methods to accurately control 7 motions of the Hannes prosthesis. First, we present a newly designed, compact HD-sEMG interface equipped with 64 dry electrodes positioned over the forearm. Then, we introduce an efficient incremental learning system enabling model adaptation on a stream of data. We thoroughly analyze multiple learning algorithms across 7 subjects, including one with limb absence, and 6 sessions held in different days covering an extended period of several months. The size and time span of the collected data represent a relevant contribution for studying long-term myocontrol performance. Therefore, we release the DELTA dataset together with our experimental code.