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基于肌电图和肌肉僵硬度的细微握力估计——肌肉特征频率与握力之间的关系

Subtle grip force estimation from EMG and muscle stiffness--relationship between muscle character frequency and grip force.

作者信息

Kasuya Masahiro, Seki Masatoshi, Kawamura Kazuya, Fujie Masakatsu G

机构信息

Graduate School of Advanced Science and Engineering, Waseda University, Tokyo 169-8555, Japan.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4116-9. doi: 10.1109/IEMBS.2011.6091022.

Abstract

A number of upper limb amputees experience difficulty in picking up a food bowl during a meal, because grip force estimation using EMG currently does not provide sufficient accuracy for this task. In this paper, we propose a grip force estimation system that allows amputees to pick up a bowl with a prosthetic hand by using the properties of muscle stiffness in addition to EMG. We have chosen a tray holding task to evaluate the proposed system. A weight is dropped on the tray and the subjects are expected to control the tray's attitude during the task. Actual grip force, EMG, and muscle stiffness are measured, and the actual measured grip force is compared with the estimated grip force for evaluation. As a result, the proposed algorithm is found to be able to estimate grip force with an error of just 18[N], which is 30% smaller than in the method that uses only EMG. From the result that the response time estimated by proposed system is even less than a human's mechanical reaction time, the effectiveness of the proposed method has been validated.

摘要

许多上肢截肢者在进餐时拿起饭碗会遇到困难,因为目前使用肌电图(EMG)进行握力估计在此任务中无法提供足够的准确性。在本文中,我们提出了一种握力估计系统,该系统除了使用EMG外,还利用肌肉刚度的特性,使截肢者能够用假手拿起碗。我们选择了托盘支撑任务来评估所提出的系统。将一个重物放在托盘上,受试者需要在任务过程中控制托盘的姿态。测量实际握力、EMG和肌肉刚度,并将实际测量的握力与估计的握力进行比较以进行评估。结果发现,所提出的算法能够以仅18[N]的误差估计握力,这比仅使用EMG的方法小30%。从所提出系统估计的响应时间甚至小于人类机械反应时间的结果来看,所提出方法的有效性得到了验证。

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