Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning
Journal:
arXiv
Published Date:
Dec 20, 2024
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.
