ArticleGait & posture2025
Predicting lower body joint moments and electromyography signals using ground reaction forces during walking and running: An artificial neural network approach.
Article in Gait & posture, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Gait stability in peripheral artery disease: a phase-dependent analysis using gait tube methodology.Frontiers in rehabilitation sciences · 2026Article
- EMG-Based Simulation for Optimization of Human-in-the-Loop Control in Simple Robotic Walking Assistance.Journal of sensor and actuator networks · 2025Article
- Modeling Dual-Task Performance: Identifying Key Predictors Using Artificial Neural Networks.Biomimetics (Basel, Switzerland) · 2025Article
- Challenges in Combining EMG, Joint Moments, and GRF from Marker-Less Video-Based Motion Capture Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Ground Reaction Forces and Joint Moments Predict Metabolic Cost in Physical Performance: Harnessing the Power of Artificial Neural Networks.Applied sciences (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThis study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to enhance the accessibility and affordability of biomechanical assessments using GRF data, thus eliminating the need for costly motion capture systems. RESEARCH QUESTION: Can ANNs use GRF data to accurately predict joint moments in the lower limbs and EMG signals?
methodsWe employed ANNs to analyze GRF data and to use them to predict joint moments (363-trials; 4-datasets) and EMG signals (63-trials; 2-datasets). We selected the EMG timeseries of 6 muscles (Biceps Femoris, Gluteus Maximus, Rectus Femoris, Medial Gastrocnemius, Soleus, and Tibialis Anterior) and joint moment timeseries in the lower limbs (ankle, knee, and hip).
resultsThe ANN models demonstrated high predictive accuracy for joint moments (R-value: 0.97, p < 0.0001) and EMG signals (R-value: 0.95, p < 0.0001) across various gait activities, including walking and running. This underscores the potential of using GRF data to understand complex biomechanical interactions, offering significant insights into human locomotion. SIGNIFICANCE: The significance of this research extends broadly, touching upon the development of portable devices, assistive technologies, and personalized rehabilitation programs. Our findings have the potential to broaden the accessibility of advanced biomechanical analysis with implications spanning disciplines such as sports science, rehabilitation, and the advancement of innovative assistive devices and exoskeletons.
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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.