Continuous Estimation of Finger Joint Angles Using Inputs from an EMG-to-Muscle Activation Model (MEとバイオサイバネティックス)

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概要

• 論文の詳細を見る

• Surface electromyography (sEMG) signals are often used in many robot and rehabilitation applications because these reflect the motor intention of users. However, inherent problems such as electromechanical delay are present in such applications. Here, we present a method to estimate finger joint angles using a neural network with inputs obtained from an EMG-to-Activation model which parameterizes this delay. Our results show overall root-mean-square errors of 5-12% between the predicted and actual joint angles. We also show results when the proposed muscle activation input is used compared to using features used by other related studies. Finally, we compare the use of a neural network to a Gaussian Process, which is a popular nonparametric Bayesian regressor that could efficiently give better prediction in this setting.

• 一般社団法人電子情報通信学会の論文
• 2012-10-04

著者

• Shibata Tomohiro

  Graduate School Of Information Science Nara Institute Of Science And Technology

• Tamei Tomoya

  Graduate School Of Information Science Nara Institute Of Science And Technology

• NGEO Jimson

  Graduate School of Information Science, Nara Institute of Science and Technology

関連論文

• Fast Reinforcement Learning for Three-Dimensional Kinetic Human-Robot Cooperation with an EMG-to-Activation Model
• Virtual Force/Tactile Sensors for Interactive Machines Using the User's Biological Signals
• Continuous Estimation of Finger Joint Angles Using Inputs from an EMG-to-Muscle Activation Model (MEとバイオサイバネティックス)

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