Estimation and Early Prediction of Grip Force Based on sEMG Signals and Deep Recurrent Neural Networks

TL;DR

This study demonstrates that deep recurrent neural networks, specifically GRU and LSTM, can accurately and rapidly predict grip force from sEMG signals, facilitating improved control of prosthetic hands.

Contribution

It introduces a deep learning approach using recurrent neural networks to predict grip force directly from sEMG signals without feature extraction, achieving high accuracy and speed.

Findings

Recurrent networks outperform nonrecurrent models in force prediction.

GRU and LSTM achieve R-squared values of 0.994 and 0.992.

Prediction rate exceeds 1300 predictions per second.

Abstract

Hands are used for communicating with the surrounding environment and have a complex structure that enables them to perform various tasks with their multiple degrees of freedom. Hand amputation can prevent a person from performing their daily activities. In that event, finding a suitable, fast, and reliable alternative for the missing limb can affect the lives of people who suffer from such conditions. As the most important use of the hands is to grasp objects, the purpose of this study is to accurately predict gripping force from surface electromyography (sEMG) signals during a pinch-type grip. In that regard, gripping force and sEMG signals are derived from 10 healthy subjects. Results show that for this task, recurrent networks outperform nonrecurrent ones, such as a fully connected multilayer perceptron (MLP) network. Gated recurrent unit (GRU) and long short-term memory (LSTM) networks can predict the gripping force with R-squared values of 0.994 and 0.992, respectively, and a prediction rate of over 1300 predictions per second. The predominant advantage of using such frameworks is that the gripping force can be predicted straight from preprocessed sEMG signals without any form of feature extraction, not to mention the ability to predict future force values using larger prediction horizons adequately. The methods presented in this study can be used in the myoelectric control of prosthetic hands or robotic grippers.