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机器人测试机器人:ALAN-Arm,一种用于测试机器人康复系统的类人手臂。

Robots testing robots: ALAN-Arm, a humanoid arm for the testing of robotic rehabilitation systems.

作者信息

Brookes Jack, Kuznecovs Maksims, Kanakis Menelaos, Grigals Arturs, Narvidas Mazvydas, Gallagher Justin, Levesley Martin

出版信息

IEEE Int Conf Rehabil Robot. 2017 Jul;2017:676-681. doi: 10.1109/ICORR.2017.8009326.

Abstract

Robotics is increasing in popularity as a method of providing rich, personalized and cost-effective physiotherapy to individuals with some degree of upper limb paralysis, such as those who have suffered a stroke. These robotic rehabilitation systems are often high powered, and exoskeletal systems can attach to the person in a restrictive manner. Therefore, ensuring the mechanical safety of these devices before they come in contact with individuals is a priority. Additionally, rehabilitation systems may use novel sensor systems to measure current arm position. Used to capture and assess patient movements, these first need to be verified for accuracy by an external system. We present the ALAN-Arm, a humanoid robotic arm designed to be used for both accuracy benchmarking and safety testing of robotic rehabilitation systems. The system can be attached to a rehabilitation device and then replay generated or human movement trajectories, as well as autonomously play rehabilitation games or activities. Tests of the ALAN-Arm indicated it could recreate the path of a generated slow movement path with a maximum error of 14.2mm (mean = 5.8mm) and perform cyclic movements up to 0.6Hz with low gain (<1.5dB). Replaying human data trajectories showed the ability to largely preserve human movement characteristics with slightly higher path length and lower normalised jerk.

摘要

作为一种为患有某种程度上肢瘫痪的个体(如中风患者)提供丰富、个性化且具成本效益的物理治疗方法,机器人技术越来越受欢迎。这些机器人康复系统通常功率很大,而且外骨骼系统可能会以一种限制的方式附着在人身上。因此,在这些设备与个体接触之前确保其机械安全性是首要任务。此外,康复系统可能会使用新型传感器系统来测量当前手臂位置。这些用于捕捉和评估患者动作的传感器系统首先需要由外部系统验证其准确性。我们展示了ALAN-Arm,这是一种人形机器人手臂,旨在用于机器人康复系统的准确性基准测试和安全测试。该系统可以连接到康复设备上,然后重放生成的或人类的运动轨迹,还能自主进行康复游戏或活动。对ALAN-Arm的测试表明,它能够以最大误差14.2毫米(平均 = 5.8毫米)重现生成的缓慢运动路径,并以低增益(<1.5分贝)执行高达0.6赫兹的循环运动。重放人类数据轨迹显示,它能够在很大程度上保留人类运动特征,只是路径长度略长,归一化加加速度略低。

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