title: 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.
authors.affiliation: Running Injury Clinic, Calgary, AB, Canada T2V 5A8; Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada T2N 1N4. Electronic address: stosis@ucalgary.ca.
authors.forename: Sean T
authors.initials: ST
authors.lastname: Osis
authors.affiliation: Running Injury Clinic, Calgary, AB, Canada T2V 5A8; Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada T2N 1N4.
authors.forename: Blayne A
authors.initials: BA
authors.lastname: Hettinga
authors.affiliation: Running Injury Clinic, Calgary, AB, Canada T2V 5A8; Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada T2N 1N4; Faculty of Nursing, University of Calgary, Calgary, AB, Canada T2N 1N4.
authors.forename: Reed
authors.initials: R
authors.lastname: Ferber
mesh_terms: D000328:Adult; D001696:Biomechanical Phenomena / Q000502:physiology*; D005080:Exercise Test; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D012016:Reference Values; D012420:Running / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D016138:Walking / Q000502:physiology*; D016474:Weight-Bearing / Q000502:physiology*
keywords: Event detection; Foot strike; Gait biomechanics; Kinematics