Papers › Deep Learning for Musculoskeletal Force Prediction
Deep Learning for Musculoskeletal Force Prediction
Lance Rane, Ziyun Ding, Alison H. McGregor, Anthony M. J. Bull
Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in the use of supervised learning to build approximate models for computationally demanding processes, with benefits in speed and flexibility. Here, we use a deep neural network to learn the mapping from movement space to muscle space. Trained on a set of kinematic, kinetic and electromyographic measurements from 156 subjects during gait, the network’s predictions of internal force magnitudes show good concordance with those derived by musculoskeletal modelling. In a separate set of experiments, training on data from the most widely known benchmarks of modelling performance, the international Grand Challenge competitions, generates predictions that better those of the winning submissions in four of the six competitions. Computational speedup facilitates incorporation into a lab-based system permitting real-time estimation of forces, and interrogation of the trained neural networks provides novel insights into population-level relationships between kinematic and kinetic factors.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| EMG Signal Prediction | Grand Challenge Competition - in Vivo Knee Loads | ImpCollege | RMSE (SE, Gluteus Medius) | 0.22 ± 0.09 | #1 of 1 | Archive leaderboard | report |
| Medial knee JRF Prediction | Grand Challenge Competition - in Vivo Knee Loads | IMP - Force plate and kinematic data | RMSE (Subject-exposed) | 186 ± 207 | #1 of 3 | Archive leaderboard | report |
| Medial knee JRF Prediction | Grand Challenge Competition - in Vivo Knee Loads | IMP - Force plate and kinematic data | RMSE (Subject-naïve) | 216 ± 136 | #1 of 3 | Archive leaderboard | report |
| Medial knee JRF Prediction | Grand Challenge Competition - in Vivo Knee Loads | IMP - Kinematic data only | RMSE (Subject-exposed) | 212 ± 213 | #2 of 3 | Archive leaderboard | report |
| Medial knee JRF Prediction | Grand Challenge Competition - in Vivo Knee Loads | IMP - Kinematic data only | RMSE (Subject-naïve) | 247 ± 119 | #2 of 3 | Archive leaderboard | report |
| Medial knee JRF Prediction | Grand Challenge Competition - in Vivo Knee Loads | IMP - Force plate data only | RMSE (Subject-exposed) | 268 ± 206 | #3 of 3 | Archive leaderboard | report |
| Medial knee JRF Prediction | Grand Challenge Competition - in Vivo Knee Loads | IMP - Force plate data only | RMSE (Subject-naïve) | 291 ± 132 | #3 of 3 | Archive leaderboard | report |
| Muscle Force Prediction | Grand Challenge Competition - in Vivo Knee Loads | ImpCollege | RMSE (SE, Gluteus Maximus) | 91 ± 72 | #1 of 1 | Archive leaderboard | report |
| Muscle Force Prediction | Grand Challenge Competition - in Vivo Knee Loads | ImpCollege | RMSE (SE, Gluteus Medius) | 196 ± 186 | #1 of 1 | Archive leaderboard | report |
| Muscle Force Prediction | Grand Challenge Competition - in Vivo Knee Loads | ImpCollege | RMSE (SE, Hamstrings) | 140 ± 175 | #1 of 1 | Archive leaderboard | report |
| Muscle Force Prediction | Grand Challenge Competition - in Vivo Knee Loads | ImpCollege | RMSE (SE, Quadriceps) | 194 ± 140 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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