Estimating Running Ground Reaction Force Curves Using Long Short-Term Memory Neural Network and Markerless Motion Capture System
10.16156/j.1004-7220.2025.05.028
- VernacularTitle:基于长短时记忆模型与无标记动作捕捉系统估算跑步地面反作用力曲线
- Author:
Yulin ZHOU
1
;
Junchen ZHAO
;
Hanjun LI
;
Huijuan SHI
;
Hui LIU
Author Information
1. 北京体育大学运动人体科学学院,北京 100084
- Publication Type:Journal Article
- Keywords:
long short-term memory model;
ground reaction forces;
markerless motion capture;
running injury
- From:
Journal of Medical Biomechanics
2025;40(5):1295-1302
- CountryChina
- Language:Chinese
-
Abstract:
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.
