Database-driven approach for Biosignal-based robot control with collaborative filtering
Database-driven approach for Biosignal-based robot control with collaborative filtering
复制标题
具有协作过滤功能的基于生物信号的机器人控制的数据库驱动方法
DOI：
10.1109/humanoids.2017.8246934
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Morimoto
中科院分区：
文献类型：
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作者：
J. Furukawa;Asuka Takai;J. Morimoto
在这项研究中，我们提出了一个databasedriven基于肌电机器人控制的扭矩估计方法。对于传统的基于EMG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工作，并限制了基于EMG的方法的应用到实际情况。为了科普这个问题，我们使用从其他用户获得的大规模数据，以避免校准过程，并提出协同过滤，估计一个新的用户的联合扭矩，通过利用以前推导的肌电信号和其他用户的联合扭矩之间的关系。为了验证我们提出的方法，我们比较了联合扭矩估计性能与标准的线性转换模型。在我们的实验中，我们控制上肢外骨骼机器人与估计的关节扭矩，我们使用16通道电极来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标准方法相当的控制性能，需要仔细的校准过程。
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.