EMG-based Estimation of Knee Torque and Angle using Recurrent Neural Network

Minh T.N. TRUONG; Seiichi SAKAMOTO; Dai OWAKI; Mitsuhiro HAYASHIBE

doi:10.1299/jsmermd.2020.1A1-F04

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)

Online ISSN : 2424-3124

2020

Session ID : 1A1-F04

DOI https://doi.org/10.1299/jsmermd.2020.1A1-F04

Conference information

Host: The Japan Society of Mechanical Engineers

Name : [in Japanese]

Date : May 27, 2020 - May 30, 2020

EMG-based Estimation of Knee Torque and Angle using Recurrent Neural Network

*Minh T.N. TRUONG, Seiichi SAKAMOTO, Dai OWAKI, Mitsuhiro HAYASHIBE

Author information

Keywords: Musculoskeletal model, Neural network, Electromyography(EMG)

CONFERENCE PROCEEDINGS RESTRICTED ACCESS

Details

Abstract

This work presents a framework for the kinetics and kinematics estimation of the human knee joints in natural movements. In this study, we employed the long short-term memory (LSTM), a special recurrent neural network (RNN) architecture, as an estimation algorithm by using electromyography (EMG) features. The results suggest a potential application for online prediction of joint kinetics and kinematics without ground force measurements.

Content from these authors

Favorites & Alerts

Corresponding author

Register with J-STAGE for free!