ArticleMedical & biological engineering & computing2022
Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Hierarchical Three-Stage Feature Selection Strategy for Efficient and Generalizable sEMG-Based Joint Torque Estimation in Athletic Training and Rehabilitation.Sensors (Basel, Switzerland) · 2026Article
- Wireless knee joint monitoring using biodegradable single-ended pressure sensor in osteoarthritis management.Science advances · 2026Article
- Optimized Signal Acquisition and Advanced AI for Robust 1D EMG Classification: A Comparative Study of Machine Learning, Deep Learning, and Reinforcement Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Review of electroencephalography and electromyography research in robotics: opportunities and challenges.Visual computing for industry, biomedicine, and art · 2026Review
- Automated Neuromuscular Assessment: Machine-Learning-Based Facial Palsy Classification Using Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- Muscle synergy analysis during badminton forehand overhead smash: integrating electromyography and musculoskeletal modeling.Frontiers in sports and active living · 2025Article
- Feasibility study on the application of HD-sEMG-based force estimation technology in the assessment of hand dysfunction in cerebral palsy.Frontiers in bioengineering and biotechnology · 2025Article
- Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture.Scientific reports · 2024Article
- A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring.Sensors (Basel, Switzerland) · 2024Article
- An Optimized Stimulation Control System for Upper Limb Exoskeleton Robot-Assisted Rehabilitation Using a Fuzzy Logic-Based Pain Detection Approach.Sensors (Basel, Switzerland) · 2024Article
- Multivariable analysis for predicting lower limb muscular strength with a hip-joint exoskeleton.Frontiers in bioengineering and biotechnology · 2024Article
- Research on the electromyography-based pattern recognition for inter-limb coordination in human crawling motion.Frontiers in neuroscience · 2024Article
- A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs.Frontiers in physiology · 2023Article
- Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation.Micromachines · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length. Graphical Abstract.
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