EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network

Abstract

One of the fundamental limitations in human biomechanics is that we cannot directly obtain joint moments during natural movements without affecting the motion. However, estimating these values is feasible with inverse dynamics computation by employing external force plates, which can cover only a small area of the plate. This work investigated the Long Short-Term Memory (LSTM) network for the kinetics and kinematics prediction of human lower limbs when performing different activities without using force plates after the learning. We measured surface electromyography (sEMG) signals from 14 lower extremities muscles to generate a 112-dimensional input vector from three sets of features: root mean square, mean absolute value, and sixth-order autoregressive model coefficient parameters for each muscle in the LSTM network. With the recorded experimental data from the motion capture system and the force plates, human motions were reconstructed in a biomechanical simulation created using OpenSim v4.1, from which the joint kinematics and kinetics from left and right knees and ankles were retrieved to serve as output for training the LSTM. The estimation results using the LSTM model deviated from labels with average R2 scores (knee angle: 97.25%, knee moment: 94.9%, ankle angle: 91.44%, and ankle moment: 85.44%). These results demonstrate the feasibility of the joint angle and moment estimation based solely on sEMG signals for multiple daily activities without requiring force plates and a motion capture system once the LSTM model is trained.

The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development

“…Consequently, LSTM is widely used owing to its capability of generating accurate outputs for time series: the action patterns of walking occur in a time series ( Song et al, 2020 ), and LSTM generates outputs based on the inputs from the past ( Greff et al, 2017 ), making it suitable for predicting joint kinematics and kinetics ( Ma et al, 2020 ). Also, the sEMG feature extraction is used widely, where features are extracted within a sliding window and used as inputs to predict joint kinetics or kinematics ( Bi et al, 2019 ; Gupta et al, 2020 ; Zhang et al, 2021 ; Chen et al, 2022 ; Rabe and Fey, 2022 ; Truong et al, 2023 ). Previous studies have shown that using sEMG features as input (e.g., Spanias et al, 2015 , 2018 ), the LSTM structure (e.g., Ren et al, 2022 ), and their combined implementations (e.g., Song et al, 2020 ) provide a successful prediction of intended motion.…”

Lower Limb Joint Torque Prediction Using Long Short-Term Memory Network and Gaussian Process Regression

“…Zhang et al [30] observed that an LSTM model could predict lower limb joint torque during various activities accurately, with a relatively low error, using sEMG signals and joint angles as inputs. Truong et al [31] extracted several sEMG features to predict joint angles and joint torque using an LSTM model when squatting, picking up an object, and sitting-standing.…”