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

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.

Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors

“…Understanding the role of external TA in both external Frontiers in Bioengineering and Biotechnology frontiersin.org impact loading and internal tibial bone loading, therefore, becomes crucial (Matijevich et al, 2019). Enhancing the evaluation of these factors through machine learning not only presents an intriguing area of research but also holds substantial potential implications for future applications in sports medicine, injury prevention, and rehabilitation strategies (Zadpoor and Nikooyan, 2011;Johnson C. D. et al, 2020;Xiang et al, 2022a;Xiang et al, 2022b;Gao et al, 2023;Lloyd et al, 2023;Uhlrich et al, 2023;Xiang et al, 2023). This systematic review aims to bridge a critical gap in our understanding of the relationship among GRF, TA, tibial bone loading, and running-related injuries, a topic of significant importance to both athletes and recreational runners.…”