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

Liangliang Xiang; Zixiang Gao; Alan Wang; Vickie Shim; Gusztáv Fekete; Yaodong Gu; Justin Fernandez

doi:10.3389/fbioe.2024.1377383

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

Front Bioeng Biotechnol. 2024 Apr 8:12:1377383. doi: 10.3389/fbioe.2024.1377383. eCollection 2024.

Authors

Liangliang Xiang^#^{1

2}, Zixiang Gao^#^{1

3}, Alan Wang^{2

4}, Vickie Shim², Gusztáv Fekete⁵, Yaodong Gu^{1

2

6}, Justin Fernandez^{2

7}

Affiliations

¹ Department of Radiology, Ningbo No. 2 Hospital, Ningbo, China.
² Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
³ Faculty of Engineering, University of Pannonia, Veszprém, Hungary.
⁴ Center for Medical Imaging, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
⁵ Vehicle Industry Research Center, Széchenyi István University, Győr, Hungary.
⁶ Faculty of Sports Science, Ningbo University, Ningbo, China.
⁷ Department of Engineering Science, The University of Auckland, Auckland, New Zealand.

^# Contributed equally.

Abstract

This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors' potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.

Keywords: impact load; inertial measurement unit (IMU) sensor; machine learning; running; tibial acceleration.

Publication types

Review

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported 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), Ningbo key R&D Program (2022Z196), Research Academy of Medicine Combining Sports, Ningbo (No. 2023001), the Project of NINGBO Leading Medical and Health Discipline (No. 2022-F15 and 2022-F22), Ningbo Natural Science Foundation (20221JCGY010532, 20221JCGY010607), Public Welfare Science and Technology Project of Ningbo, China (2021S134), Zhejiang Rehabilitation Medical Association Scientific Research Special Fund (ZKKY2023001), and K. C. Wong Magna Fund in Ningbo University. LX and ZG are being sponsored by the China Scholarship Council (CSC).