基于生物信号的机器人协同滤波控制的数据库驱动方法

J. Furukawa, Asuka Takai, J. Morimoto
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引用次数: 1

摘要

在这项研究中,我们提出了一种数据库驱动的基于肌电图的机器人控制力矩估计方法。对于传统的基于肌电图的控制器,需要仔细校准扭矩估计模型以控制具有多个自由度的机器人。然而,这样的校准过程需要大量的努力,并且限制了基于肌电图的方法在实际情况中的应用。为了解决这个问题,我们使用从其他用户获取的大量数据来避免校准过程,并提出协同滤波,利用先前导出的肌电信号与其他用户关节扭矩之间的关系来估计新用户的关节扭矩。为了验证我们提出的方法,我们将关节转矩估计性能与标准线性转换模型进行了比较。在我们的实验中,我们用估计的关节扭矩控制上肢外骨骼机器人,并使用16-ch电极测量受试者的肌电信号。在比较中,我们提出的方法显示出与需要仔细校准过程的标准方法相当的控制性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Database-driven approach for Biosignal-based robot control with collaborative filtering
In this study, we propose a databasedriven torque estimation approach for EMG-based robot control. For conventional EMG-based controllers, torque estimation models need to be carefully calibrated to control robots that have multiple degrees of freedom. However, such a calibration procedure requires significant effort and restricts the applications of EMG-based methods to practical situations. To cope with this issue, we use large-scale data acquired from other users to avoid the calibration process and propose collaborative filtering to estimate the joint torque of a new user by exploiting the previously derived relationships between the EMG signals and the joint torque of other users. To validate our proposed method, we compared the joint torque estimation performance with a standard linear conversion model. In our experiments, we controlled an upper-limb exoskeleton robot with the estimated joint torque where we used 16-ch electrodes to measure the EMG signals of subjects. In a comparison, our proposed method showed comparable control performance with the standard approach that requires a careful calibration process.
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