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

Area of Science:

• Biomechanics
• Medical Technology
• Data Science

Background:

• Gait analysis is vital for assessing motor function, fall risk, and neurological disorders like Parkinson's disease.
• Inertial measurement units (IMUs) offer a precise, portable, and cost-effective alternative to traditional optical gait analysis systems.
• The growing aging population and focus on sports health increase the demand for advanced gait analysis solutions.

Purpose of the Study:

• To provide a comprehensive guide for selecting IMU-based gait analysis methodologies across various applications.
• To synthesize current IMU-based methods from a sensor-placement perspective, differentiating from existing reviews.
• To propose a decision framework for optimal sensor locations and algorithms based on application needs and computational limits.

Main Methods:

• A systematic literature search was performed across major databases (CNKI, Wanfang, PubMed, Web of Science) from January 2019 to December 2025.
• Relevant English and Chinese keywords related to IMUs and gait analysis were used.
• Foundational studies predating 2019 were included to ensure methodological completeness, followed by a systematic review.

Main Results:

• A comprehensive comparison of IMU-based gait analysis methods based on body sensor placement (foot, leg, chest) was conducted.
• Significant variations in algorithm complexity, accuracy, and suitability were observed across different populations and scenarios.
• An evidence-based framework was established for selecting optimal gait analysis solutions.

Conclusions:

• Sensor placement significantly impacts the performance of IMU-based gait analysis.
• The developed framework aids researchers and clinicians in choosing appropriate IMU sensor locations and algorithms.
• This study facilitates tailored gait analysis solutions for diverse real-world applications.