Predicting ground contact events for a continuum of gait types: An application of targeted machine learning using principal component analysis
Abstract
An ongoing challenge in the application of gait analysis to clinical settings is the standardized detection of temporal events, with unobtrusive and cost-effective equipment, for a wide range of gait types. The purpose of the current study was to investigate a targeted machine learning approach for the prediction of timing for foot strike (or initial contact) and toe-off, using only kinematics for walking, forefoot running, and heel-toe running. Data were categorized by gait type and split into a training set (∼30%) and a validation set (∼70%). A principal component analysis was performed, and separate linear models were trained and validated for foot strike and toe-off, using ground reaction force data as a gold-standard for event timing. Results indicate the model predicted both foot strike and toe-off timing to within 20ms of the gold-standard for more than 95% of cases in walking and running gaits. The machine learning approach continues to provide robust timing predictions for clinical use, and may offer a flexible methodology to handle new events and gait types.
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Principal Component Analysis (PCA) performs linear dimensionality reduction preserving maximal variance direction, eliminates redundancy, and enhances computational efficiency for multi-channel signal dimensionality reduction (e.g., EMG).
“| Principal Component | Linear dimensionality reduction preserving | Eliminate redundancy, enhance | Multi-channel signal dimensionality reduction |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [257]
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- Authors
- Osis, S. T., Hettinga, B. A., & Ferber, R.
- Affiliations
- University of Calgary (CA)
- Corresponding author
- Osis, Sean T.
- ORCID
- Ferber, Reed
- Venue
- Gait & Posture
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 46
- Pages
- 86-90
- ISSN
- 0966-6362 (ISSN-L)
- OpenAlex
- W2288603929
- Cited by
- 37
- References
- 13
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 7 · 2019: 3 · 2018: 3 · 2017: 2 · 2016: 1
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Gait & Posture (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council (1030390)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- gait event detection, foot strike, toe-off, gait analysis, walking, running, ground reaction force, kinematics, principal component analysis, linear models
- MeSH terms
- Machine Learning, Adult, Biomechanical Phenomena, Exercise Test, Female, Gait, Humans, Male, Middle Aged, Reference Values, Running, Signal Processing, Computer-Assisted, Linear Models, Walking, Weight-Bearing, Principal Component Analysis
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