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Estimating Running Ground Reaction Force Curves Using Long Short-Term Memory Neural Network and Markerless Motion Capture System
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Yulin ZHOU1, Junchen ZHAO1, Hanjun LI1, 2, Huijuan SHI1, 2, Hui LIU1, 2
Journal of Medical Biomechanics | 2025, 40(5) : 1295 - 1302
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Journal of Medical Biomechanics | 2025, 40(5): 1295-1302
Original Articles
Estimating Running Ground Reaction Force Curves Using Long Short-Term Memory Neural Network and Markerless Motion Capture System
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Yulin ZHOU1, Junchen ZHAO1, Hanjun LI1, 2, Huijuan SHI1, 2, Hui LIU1, 2
Affiliations
  • 1.School of Sport Science, Beijing Sport University, Beijing 100084, China
  • 2.Key Laboratory for Performance Training & Recovery of General Administration of Sport, Beijing Sport University, Beijing 100084, China
Published: 2025-10-01 doi: 10.16156/j.1004-7220.2025.05.028
Outline
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Objective

By applying the long short-term memory (LSTM) neural network model and using lower body landmark coordinates obtained from a markerless motion capture system as inputs, to estimate ground reaction force (GRF) curves during running.

Methods

The video images and GRF data of 59 amateur runners during running were collected by the markerless motion capture system and three-dimensional (3D) force plates. The LSTM model was established, and the 3D coordinates of 11 lower body landmarks, obtained via the Theia3D markerless system, were used as inputs to estimate the 3D GRF curves during the stance of running. The estimation performance was evaluated using correlation coefficients r, root mean square error (RMSE), and normalized root mean square error (nRMSE) by comparing LSTM model estimation and force plate measurement. Statistical parametric mapping was used to analyze differences in GRF curves estimated by the LSTM model and measured by the force plate, while paired t-tests were used to assess differences in GRF characteristics between model estimation and actual measurement.

Results

A strong correlation (r>0.85, P<0.001) and lower error (RMSE<0.3 body weight, nRMSE<15%) was found between the LSTM model estimation and actual measurements. No significant difference was found in GRF curve intervals between LSTM model estimation and actual measurements. There was no significant difference in GRF characteristics between LSTM model estimation and actual measurements (P>0.05).

Conclusions

Based on the LSTM model, the 3D GRF curves can be effectively estimated by lower body landmark coordinates obtained from the makerless motion capture system, thereby acquiring the highly accurate GRF characteristics. The LSTM model developed in this study can be used to monitor injury risks during running in outdoor environments.

long short-term memory model  /  ground reaction forces  /  markerless motion capture  /  running injury
Yulin ZHOU, Junchen ZHAO, Hanjun LI, Huijuan SHI, Hui LIU. Estimating Running Ground Reaction Force Curves Using Long Short-Term Memory Neural Network and Markerless Motion Capture System[J]. Journal of Medical Biomechanics, 2025 , 40 (5) : 1295 -1302 . DOI: 10.16156/j.1004-7220.2025.05.028
Year 2025 volume 40 Issue 5
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Article Info
doi: 10.16156/j.1004-7220.2025.05.028
  • Receive Date:2025-03-02
  • Online Date:2026-03-27
  • Published:2025-10-01
Article Data
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History
  • Received:2025-03-02
  • Revised:2025-03-25
Funding
Affiliations
    1.School of Sport Science, Beijing Sport University, Beijing 100084, China
    2.Key Laboratory for Performance Training & Recovery of General Administration of Sport, Beijing Sport University, Beijing 100084, China
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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