高密度表面筋電図とインクリメンタル学習による長期的な上肢義手の筋電制御
Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning
64個の乾式電極を備えた高密度sEMGインターフェースとインクリメンタル学習を組み合わせ、Hannes義手の7動作を長期間安定して制御するシステムを開発し、DELTAデータセットを公開した。
著者: Dario Di Domenico, Nicolò Boccardo, Andrea Marinelli, Michele Canepa, Emanuele Gruppioni, Matteo Laffranchi, Raffaello Camoriano
分類: cs.RO, cs.LG
原文アブストラクト
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.
