IMU-based gait analysis methods: a systematic review of techniques for different body locations.

Authors

Leqin Chen, Ruiwu Guo, Ruiwen Guo, Yuting Chen, Yinfeng Wang, Shihao Zhang, Qingtong Zhang

Published in

Frontiers in sports and active living. Volume 8. Pages 1854615. Epub Jul 31, 2026.

Abstract

Gait analysis is a crucial tool for evaluating human motor function, with its applications expanding across clinical and health-monitoring domains. The growth of the aging global population and an increasing emphasis on sports health have positioned gait abnormalities as significant biomarkers for assessing fall risk and neurological disorders, including Parkinson's disease. Compared with conventional optical systems, inertial measurement units (IMUs) provide a high-precision, cost-effective, and portable alternative, thereby facilitating the practical capture of gait data in real-world contexts.
This review aims to deliver a comprehensive guide for selecting the most suitable IMU-based gait analysis methodologies, tailored to various application scenarios. Unlike existing reviews, which primarily focus on specific algorithms or populations, this study uniquely synthesizes current IMU-based methods from a sensor-placement perspective. Furthermore, we propose a practical decision framework to guide researchers and clinicians in selecting the optimal sensor locations and algorithms tailored to specific application scenarios and computational constraints.
A systematic search was conducted across the China National Knowledge Infrastructure, Wanfang, PubMed, and Web of Science databases from January 2019 to December 2025 using relevant English and Chinese keywords related to IMUs and gait analysis. To ensure methodological completeness in this mature field, foundational studies published before 2019 were additionally included through targeted supplementary retrieval. The retrieved studies underwent a systematic review.
This paper provides a comprehensive comparison of IMU-based gait analysis methods across various sensor placements on the body (e.g., foot, leg, chest). The findings reveal substantial differences in algorithm complexity, accuracy, and suitability across distinct populations and scenarios. This study establishes an evidence-based framework for identifying optimal gait analysis solutions to address diverse application needs.

PMID:
42602715
Bibliographic data and abstract were imported from PubMed on 15 Aug 2026.

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