Towards Data-Driven Autonomous Robot-Assisted Physical Rehabilitation Therapy

Wen Wang, Rochelle J. Mendonca, Konrad Paul Kording, Mikael Avery, M. Johnson
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引用次数: 4

Abstract

Task-oriented therapy consists of three stages: demonstration, observation and assistance. While demonstration using robots has been extensively studied, the other two stages rarely involve robots. This paper focuses on the transition between observation and assistance. More specifically, we tackle the robot’s decision making problem of whether to assist a patient or not based on the observation. The proposed method is to train a discrete tunnel shape 3-D decision boundary through correct demonstration to classify motions. Additional conditions such as slow progress, self correction and overshot motions are taken into account of the decision making. Preliminary experiments have been performed on BAXTER robot for a cup reaching task. The BAXTER robot is programmed to react according to the decision boundary. It assists the patient when the patient’s hand position is determined by the proposed algorithm to be unacceptable. Multiple cases including correct motion, continuous assistance, overshot, misaim and slow progress are tested. Results have confirmed the feasibility of the proposed method, which can reduce the current shortage of physical rehabilitation therapists.
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迈向数据驱动的自主机器人辅助物理康复治疗
任务导向治疗包括示范、观察和协助三个阶段。虽然使用机器人的演示已被广泛研究,但其他两个阶段很少涉及机器人。本文的重点是观察和援助之间的过渡。更具体地说,我们根据观察来解决机器人是否帮助病人的决策问题。该方法是通过正确的演示训练离散的隧道形状三维决策边界,对运动进行分类。额外的条件,如缓慢的进展,自我纠正和超冲运动的决策被考虑在内。在BAXTER机器人上进行了一次杯子取物任务的初步实验。对BAXTER机器人进行编程,使其根据决策边界做出反应。当提出的算法确定患者的手部位置不可接受时,它会帮助患者。多种情况包括正确的动作,持续的辅助,过冲,错瞄准和缓慢的进展进行测试。结果证实了所提出方法的可行性,可以缓解目前物理康复治疗师的短缺。
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