Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations
Open accessAbstract
Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person's cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life, individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.
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Physilog inertial sensors have good technical capability compared to reference systems in healthy and clinical settings.
“In general Physilog inertial sensors have good technical capability compared to reference systems in healthy and clinical settings (39, 40).”
From The instrumented single leg stance test detects early balance impairment in people with multiple sclerosis (2023) · cites this paper as [40]
The Experimental Setup identifies Physilog 5 IMUs and an OptoGait reference system; the Abstract reports accurate stride-length and stride-time estimates from the IMU-based algorithm. But the study tested five healthy subjects, not a clinical population, and used the sensors’ raw data rather than validating the manufacturer’s gait-analysis software. (LLM, conf=0.88)
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- Authors
- Zhou, L., Tunca, C., Fischer, E., Brahms, C. M., Ersoy, C., Granacher, U., & Arnrich, B.
- Affiliations
- University of Potsdam (DE)
- Boğaziçi University (TR)
- Venue
- 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2020-07
- Volume
- 2020
- Pages
- 4229-4232
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W3082879976
- Cited by
- 63
- References
- 19
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 15 · 2023: 17 · 2022: 7 · 2021: 11
- FWCI
- 15.89
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (01IS19066)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- inertial measurement unit, gait analysis, stride length, stride time, algorithm validation, walking patterns, wearable sensors
- MeSH terms
- Gait Analysis, Algorithms, Gait, Humans, Reproducibility of Results, Walking
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