Automated recognition of asymmetric gait and fatigue gait using ground reaction force data

Zixiang Gao; Yining Zhu; Yufei Fang; Gusztáv Fekete; András Kovács; Julien S Baker; Minjun Liang; Yaodong Gu

doi:10.3389/fphys.2023.1159668

Automated recognition of asymmetric gait and fatigue gait using ground reaction force data

Front Physiol. 2023 Mar 7:14:1159668. doi: 10.3389/fphys.2023.1159668. eCollection 2023.

Authors

Zixiang Gao^{1

2

3}, Yining Zhu⁴, Yufei Fang¹, Gusztáv Fekete³, András Kovács², Julien S Baker⁵, Minjun Liang^{1

6}, Yaodong Gu¹

Affiliations

¹ Research Academy of Medicine Combining Sports, Ningbo No. 2 Hospital, Ningbo, China.
² Faculty of Engineering, University of Pannonia, Veszprém, Hungary.
³ Savaria Institute of Technology, Eötvös Loránd University, Szombathely, Hungary.
⁴ Faculty of Sports Science, Ningbo University, Ningbo, China.
⁵ Department of Sport and Physical Education, Hong Kong Baptist University, Kowloon, Hong Kong SAR, China.
⁶ Department of Physical and Health Education, Udon Thani Rajabhat University, Udon Thani, Thailand.

Abstract

Introduction: The purpose of this study was to evaluate the effect of running-induced fatigue on the characteristic asymmetry of running gait and to identify non-linear differences in bilateral lower limbs and fatigued gait by building a machine learning model. Methods: Data on bilateral lower limb three-dimensional ground reaction forces were collected from 14 male amateur runners before and after a running-induced fatigue experiment. The symmetry function (SF) was used to assess the degree of symmetry of running gait. Statistical parameter mapping (Paired sample T-test) algorithm was used to examine bilateral lower limb differences and asymmetry changes pre- and post-fatigue of time series data. The support vector ma-chine (SVM) algorithm was used to recognize the gait characteristics of both lower limbs before and after fatigue and to build the optimal algorithm model by setting different kernel functions. Results: The results showed that the ground reaction forces were asymmetrical (SF > 0.5) both pre-and post-fatigue and mainly concentrated in the medial-lateral direction. The asymmetry of the medial-lateral direction increased significantly after fatigue (p < 0.05). In addition, we concluded that the polynomial kernel function could make the SVM model the most accurate in classifying left and right gait features (accuracy of 85.3%, 82.4%, and 82.4% in medial-lateral, anterior-posterior and vertical directions, respectively). Gaussian radial basis kernel function was the optimal kernel function of the SVM algorithm model for fatigue gait recognition in the medial-lateral and vertical directions (accuracy of 54.2% and 62.5%, respectively). Moreover, polynomial was the optimal kernel function of the anterior-posterior di-rection (accuracy = 54.2%). Discussion: We proved in this study that the SVM algorithm model depicted good performance in identifying asymmetric and fatigue gaits. These findings can provide implications for running injury prevention, movement monitoring, and gait assessment.

Keywords: fatigue gait; running; statistical parameter mapping; support vector machine; symmetry function.

Grants and funding

This study was sponsored by the Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars (LR22A020002), Zhejiang Provincial Key Research and Development Program of China (2021C03130), Zhejiang Provincial Natural Science Foundation (LTGY23H040003), Research Academy of Medicine Combining Sports, Ningbo (No.2023001), the Project of NINGBO Leading Medical & Health Discipline (No.2022-F15, No.2022-F22), Ningbo Natural Science Foundation (20221JCGY010532, 20221JCGY010607), Public Welfare Science & Technology Project of Ningbo, China (2021S134), and K. C. Wong Magna Fund in Ningbo University.