Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking
Abstract
The aim of this study was to investigate if a machine learning algorithm utilizing triaxial accelerometer, gyroscope, and magnetometer data from an inertial motion unit (IMU) could detect surface- and age-related differences in walking. Seventeen older (71.5 ± 4.2 years) and eighteen young (27.0 ± 4.7 years) healthy adults walked over flat and uneven brick surfaces wearing an inertial measurement unit (IMU) over the L5 vertebra. IMU data were binned into smaller data segments using 4-s sliding windows with 1-s step lengths. Ninety percent of the data were used as training inputs and the remaining ten percent were saved for testing. A deep learning network with long short-term memory units was used for training (fully supervised), prediction, and implementation. Four models were trained using the following inputs: all nine channels from every sensor in the IMU (fully trained model), accelerometer signals alone, gyroscope signals alone, and magnetometer signals alone. The fully trained models for surface and age outperformed all other models (area under the receiver operator curve, AUC = 0.97 and 0.96, respectively; p ≤ .045). The fully trained models for surface and age had high accuracy (96.3, 94.7%), precision (96.4, 95.2%), recall (96.3, 94.7%), and f1-score (96.3, 94.6%). These results demonstrate that processing the signals of a single IMU device with machine-learning algorithms enables the detection of surface conditions and age-group status from an individual's walking behavior which, with further learning, may be utilized to facilitate identifying and intervening on fall risk.
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Completely trained models for surface and age with complete IMU data had good precision (96.4, 95.2%), recall (96.3, 94.7%), f1-score (96.3, 94.6%) and accuracy (96.3, 94.6%).
“The results revealed that completely trained models for surface and age with complete IMU data had good precision (96.4, 95.2%), recall (96.3, 94.7%), f1-score (96.3, 94.6%) and accuracy (96.3, 94.6%). (96.3, 94.7%) [186].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [186]
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- Authors
- Hu, B., Dixon, P. C., Jacobs, J. V., Dennerlein, J. T., & Schiffman, J. M.
- Affiliations
- Harvard University (US)
- Liberty Mutual Research Institute for Safety (US)
- Northeastern University (US)
- Corresponding author
- Hu, B.
- Venue
- Journal of Biomechanics
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 71
- Pages
- 37-42
- ISSN
- 0021-9290 (ISSN-L)
- OpenAlex
- W2782697586
- Semantic Scholar
- 732fc353d438b503c6897995eeead0979eabc701
- Cited by
- 99
- References
- 40
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 21 · 2023: 9 · 2022: 16 · 2021: 13 · 2020: 14 · 2019: 14 · 2018: 4
- FWCI
- 5.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Biomechanics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Fonds de Recherche Québec – Santé post-doctoral training award (Dixon (33358)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- wearable inertial sensors, older adults, long short-term memory networks, accelerometer, gyroscope, magnetometer, fall risk assessment
- MeSH terms
- Machine Learning, Fitness Trackers, Wearable Electronic Devices, Deep Learning, Adult, Age Factors, Aged, Aging, Algorithms, Female, Humans, Male, Models, Biological, Motion, Walking, Young Adult
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