ArticleSensors (Basel, Switzerland)2023
EMG-Based Estimation of Lower Limb Joint Angles and Moments Using Long Short-Term Memory Network.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 32 citations in OpenAlex.
- Multi-Source Motion Inputs and FA-TA-BiLSTM for Lower-Limb Joint Angle Prediction.Sensors (Basel, Switzerland) · 2026Article
- NewRep: a classification model for online pattern recognition based on HD-EMG signals.Medical & biological engineering & computing · 2026Article
- Generalization of Knee Joint Moment Prediction During Drop Vertical Jumps Under Graded Visuo-Proprioceptive Conflict: The Role of Multijoint Kinematics Across Validation Frameworks.Bioengineering (Basel, Switzerland) · 2026Article
- Sagittal-Plane Knee Flexion Moment Estimation Using a Lightweight Deep Learning Framework Based on Sequential Surface EMG Feature Frames.Sensors (Basel, Switzerland) · 2026Article
- Long short-term attention memory (LSTAM): a global-feature-integrated model for joint moment prediction in human rehabilitation.Scientific reports · 2026Article
- A Knee Rehabilitation Exercises Dataset for Postural Assessment using Wearable Devices.Scientific data · 2025Article
- Lower Limb Joint Torque Prediction Using Long Short-Term Memory Network and Gaussian Process Regression.Sensors (Basel, Switzerland) · 2023Article
- The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.