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肌电控制机器人上肢假肢:一项可行性研究。

Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study.

机构信息

MENRVA Research Group, School of Engineering Science, Simon Fraser University , Burnaby, BC , Canada.

Barber Prosthetics Clinic , Vancouver, BC , Canada.

出版信息

Front Bioeng Biotechnol. 2016 Mar 8;4:18. doi: 10.3389/fbioe.2016.00018. eCollection 2016.

Abstract

Advancement in assistive technology has led to the commercial availability of multi-dexterous robotic prostheses for the upper extremity. The relatively low performance of the currently used techniques to detect the intention of the user to control such advanced robotic prostheses, however, limits their use. This article explores the use of force myography (FMG) as a potential alternative to the well-established surface electromyography. Specifically, the use of FMG to control different grips of a commercially available robotic hand, Bebionic3, is investigated. Four male transradially amputated subjects participated in the study, and a protocol was developed to assess the prediction accuracy of 11 grips. Different combinations of grips were examined, ranging from 6 up to 11 grips. The results indicate that it is possible to classify six primary grips important in activities of daily living using FMG with an accuracy of above 70% in the residual limb. Additional strategies to increase classification accuracy, such as using the available modes on the Bebionic3, allowed results to improve up to 88.83 and 89.00% for opposed thumb and non-opposed thumb modes, respectively.

摘要

辅助技术的进步使得多灵巧上肢机器人假肢在商业上得以应用。然而,目前用于检测用户控制此类先进机器人假肢意图的技术性能相对较低,限制了它们的使用。本文探讨了力肌电图(FMG)作为一种有前途的替代表面肌电图的方法。具体来说,研究了 FMG 用于控制商业上可用的机器人手 Bebionic3 的不同抓握方式。四名男性桡骨截肢受试者参与了这项研究,并制定了一项方案来评估 11 种抓握方式的预测准确性。检查了从 6 到 11 种抓握方式的不同组合。结果表明,使用 FMG 可以对日常生活活动中重要的 6 种基本抓握方式进行分类,在残肢上的准确率超过 70%。为了提高分类准确性的其他策略,例如使用 Bebionic3 上的可用模式,可以将反对拇指和非反对拇指模式的结果分别提高到 88.83%和 89.00%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffdc/4782664/66cd36fcab0a/fbioe-04-00018-g001.jpg

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