ArticleSensors (Basel, Switzerland)2025
A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.Annals of medicine · 2026Review
- Real-time continuous assessment of fatigue from surface electromyography with deep learning for training load regulation in elite cyclists.BMC sports science, medicine & rehabilitation · 2026Article
- Real-Time Physiological Fatigue Prediction for Human-Robot Collaborative Manufacturing Using Wearable Sensor Fusion and Hybrid Deep Learning: An In Silico Digital Twin Study.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning for Sensor-Based Sport Performance and Health Monitoring: A Review of Wearable, Vision-Based, and Multimodal Sensing Approaches.Sensors (Basel, Switzerland) · 2026Review
- Feature-Level Fusion of Surface Electromyography and Mechanomyography Signals for MVC-Normalized Shoulder Abduction Force-Level Classification in Healthy Adults.Sensors (Basel, Switzerland) · 2026Article
- Palm sEMG-based user identification during doorknob rotation using a convolutional neural network.Scientific reports · 2026Article
- Fusion of RR Interval Dynamics and HRV Multidomain Signatures Using Multimodal Neural Models for Metabolic Syndrome Classification.Medical sciences (Basel, Switzerland) · 2026Article
- Lower-limb prosthetic mechanisms: recent progress, control innovations and barriers to real-world adoption.Biomedical engineering online · 2026Review
- Prediction of Three-Dimensional Ground Reaction Forces in the Golf Swing Using Wearable Inertial Measurement Units and Biomimetic Deep Learning Models.Biomimetics (Basel, Switzerland) · 2026Article
- Action-Aware Multimodal Wavelet Fusion Network for Quantitative Elbow Motor Function Assessment Using sEMG and Robotic Kinematics.Sensors (Basel, Switzerland) · 2026Article
- Bioinformatics-Inspired IMU Stride Sequence Modeling for Fatigue Detection Using Spectral-Entropy Features and Hybrid AI in Performance Sports.Sensors (Basel, Switzerland) · 2026Article
- Research progress in exercise-induced fatigue monitoring and injury early warning based on flexible sensing textiles and deep learning.Frontiers in bioengineering and biotechnology · 2026Review
- A distributed event-triggered framework for coordinating athlete autonomic states via wearable sensor networks.Frontiers in psychology · 2026Article
- Kalman-Based Joint Analysis of IMU and Plantar-Pressure Data During Speed-Skating Slideboard Training.Sensors (Basel, Switzerland) · 2026Article
- Biomechanical Monitoring of Exercise Fatigue Using Wearable Devices: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Machine Learning-Driven Muscle Fatigue Estimation in Resistance Training with Assistive Robotics.Sensors (Basel, Switzerland) · 2025Article
- Muscle Strength Estimation of Key Muscle-Tendon Units During Human Motion Using ICA-Enhanced sEMG Signals and BP Neural Network Modeling.Sensors (Basel, Switzerland) · 2025Article
- Study on Centroid Height Prediction of Non-Rigid Vehicle Based on Deep Learning Combined Model.Sensors (Basel, Switzerland) · 2025Article
- Advancements in the application of multimodal monitoring and machine learning for the development of personalized therapeutic strategies in traumatic brain injury.Frontiers in human neuroscience · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Physical fatigue significantly impacts safety and performance across industrial, athletic, and medical domains, yet its detection remains challenging due to individual variability and limited generalizability of existing methods. This study introduces a multimodal fatigue detection system integrating surface electromyography (sEMG) and inertial measurement unit (IMU) signals, processed through a hybrid convolutional neural network-long short-term memory-attention (CNN-LSTM-Attention) model. Fatigue was induced in 35 healthy participants via step-up-and-down exercises, with gait data collected during natural walking before and after fatigue. The model leverages sEMG from the gastrocnemius lateralis and IMU-derived jerk signals from the tibialis anterior and rectus femoris to classify fatigue states. Evaluated using leave-one-subject-out cross-validation (LOSOCV), the system achieved an accuracy of 87.94% with bilateral EMG signals and a balanced recall of 87.94% for fatigued states using a combined IMU-EMG approach. These results highlight the system's robustness for personalized fatigue monitoring, surpassing traditional subject-dependent methods by addressing inter-individual differences.
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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.