Toyama Hiroaki, Kawamoto Hiroaki, Sankai Yoshiyuki
Center for Cybernics Research, University of Tsukuba, Tsukuba, Japan.
CYBERDYNE, Inc., Tsukuba, Japan.
Front Robot AI. 2024 Oct 22;11:1455582. doi: 10.3389/frobt.2024.1455582. eCollection 2024.
A robot hand-arm that can perform various tasks with the unaffected arm could ease the daily lives of patients with a single upper-limb dysfunction. A smooth interaction between robot and patient is desirable since their other arm functions normally. If the robot can move in response to the user's intentions and cooperate with the unaffected arm, even without detailed operation, it can effectively assist with daily tasks. This study aims to propose and develop a cybernic robot hand-arm with the following features: 1) input of user intention via bioelectrical signals from the paralyzed arm, the unaffected arm's motion, and voice; 2) autonomous control of support movements; 3) a control system that integrates voluntary and autonomous control by combining 1) and 2) to thus allow smooth work support in cooperation with the unaffected arm, reflecting intention as a part of the body; and 4) a learning function to provide work support across various tasks in daily use. We confirmed the feasibility and usefulness of the proposed system through a pilot study involving three patients. The system learned to support new tasks by working with the user through an operating function that does not require the involvement of the unaffected arm. The system divides the support actions into movement phases and learns the phase-shift conditions from the sensor information about the user's intention. After learning, the system autonomously performs learned support actions through voluntary phase shifts based on input about the user's intention via bioelectrical signals, the unaffected arm's motion, and by voice, enabling smooth collaborative movement with the unaffected arm. Experiments with patients demonstrated that the system could learn and provide smooth work support in cooperation with the unaffected arm to successfully complete tasks they find difficult. Additionally, the questionnaire subjectively confirmed that cooperative work according to the user's intention was achieved and that work time was within a feasible range for daily life. Furthermore, it was observed that participants who used bioelectrical signals from their paralyzed arm perceived the system as part of their body. We thus confirmed the feasibility and usefulness of various cooperative task supports using the proposed method.
一个能够用未受影响的手臂执行各种任务的机器人手臂，可以减轻单侧上肢功能障碍患者的日常生活负担。由于患者的另一只手臂功能正常，因此机器人与患者之间的顺畅交互是理想的。如果机器人能够根据用户的意图移动并与未受影响的手臂协作，即使没有详细的操作，它也能有效地协助日常任务。本研究旨在提出并开发一种具有以下特点的控制论机器人手臂：1）通过来自瘫痪手臂的生物电信号、未受影响手臂的运动以及语音输入用户意图；2）自主控制支撑动作；3）一种控制系统，通过结合1）和2）来整合自主控制和自愿控制，从而在与未受影响的手臂协作时实现顺畅的工作支持，将意图反映为身体的一部分；4）一种学习功能，为日常使用中的各种任务提供工作支持。我们通过一项涉及三名患者的初步研究，证实了所提出系统的可行性和实用性。该系统通过一种不需要未受影响的手臂参与的操作功能，与用户合作来学习支持新任务。该系统将支撑动作划分为运动阶段，并从有关用户意图的传感器信息中学习相移条件。学习后，系统根据通过生物电信号、未受影响手臂的运动以及语音输入的用户意图，通过自愿相移自主执行所学的支撑动作，从而与未受影响的手臂实现顺畅的协同运动。对患者的实验表明，该系统可以学习并与未受影响的手臂协作提供顺畅的工作支持，以成功完成他们认为困难的任务。此外，问卷调查主观上证实了根据用户意图的协作工作得以实现，并且工作时间在日常生活的可行范围内。此外，观察到使用瘫痪手臂生物电信号的参与者将该系统视为身体的一部分。因此，我们证实了使用所提出方法进行各种协作任务支持的可行性和实用性。
