A CNN-LSTM Hybrid Framework for Wrist Kinematics Estimation Using Surface Electromyography

TL;DR

This paper introduces a CNN-LSTM hybrid model that effectively captures both spatial and temporal features from surface electromyography to improve wrist movement estimation, outperforming traditional methods.

Contribution

The novel CNN-LSTM framework combines deep feature extraction with sequence modeling for enhanced sEMG-based wrist kinematics estimation.

Findings

CNN-LSTM outperforms CNN and traditional methods in accuracy.

Significant improvements in complex wrist movement estimation.

Effective in both intra-session and inter-session evaluations.

Abstract

Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNNLSTM hybrid framework to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN and conventional machine learning approaches, particularly when complex wrist movements are activated.