Shared human–robot proportional control of a dexterous myoelectric prosthesis

控制(管理) 机器人 假肢 计算机科学 模拟 物理医学与康复 医学 人工智能
作者
Katie Zhuang,Nicolas Le Sommer,Vincent Mendez,Saurav Aryan,Emanuele Formento,Edoardo D’Anna,Fiorenzo Artoni,Francesco M. Petrini,Giuseppe Granata,Giovanni Cannaviello,Wassim Raffoul,Aude Billard,Silvestro Micera
出处
期刊:Nature Machine Intelligence [Nature Portfolio]
卷期号:1 (9): 400-411 被引量:129
标识
DOI:10.1038/s42256-019-0093-5
摘要

Myoelectric prostheses allow users to recover lost functionality by controlling a robotic device with their remaining muscle activity. Such commercial devices can give users a high level of autonomy, but still do not approach the dexterity of the intact human hand. Here we present a method to control a robotic hand, shared between user intention and robotic automation. The algorithm allows user-controlled movements when high dexterity is desired, but also assisted grasping when robustness is paramount. This combination of features is currently lacking in commercial prostheses and can greatly improve prosthesis usability. First, we design and test a myoelectric proportional controller that can predict multiple joint angles simultaneously and with high accuracy. We then implement online control with both able-bodied and amputee subjects. Finally, we present a shared control scheme in which robotic automation aids in object grasping by maximizing the contact area between the hand and the object, greatly increasing grasp success and object hold times in both a virtual and a physical environment. Our results present a viable method of prosthesis control implemented in real time, for reliable articulation of multiple simultaneous degrees of freedom. A combination of engineering advances shows promise for myoelectric prosthetic hands that are controlled by a user’s remaining muscle activity. Fine finger movements are decoded from surface electromyograms with machine learning algorithms and this is combined with a robotic controller that is active only during object grasping to assist in maximizing contact. This shared control scheme allows user-controlled movements when high dexterity is desired, but also assisted grasping when robustness is required.
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