Unsupervised Gait Detection Using Biomechanical Restrictions

Quantification of human gait with sensors has enormous potential in health and rehabilitation applications. Objective measurement of gait features in the home and community can reveal the true nature of impact of disease on activities of daily living or response to interventions. Previously reported gait event detection methods have achieved good success, yet can produce errors in some irregular gait patterns. In this paper, we propose a novel unsupervised detection of gait events and gait duration by combining two exclusive processes: (i) exploration of gait event candidates based on iterative running of existing methods with changing parameters and, (ii) selection of the candidate which satisfies gait-specific biomechanical restrictions (e. g., when one leg is in swing, another leg is likely to be in stance). We evaluated this approach using data from a single-axis gyroscope on the left and right ankles in three experimental conditions. The proposed method decreased the timing error for detection of gait events (toe off and heel strike) in irregular gait patterns compared with the conventional method. It also improved the accuracy of measurement of gait duration in a longitudinal free-living dataset and distinguishing gait from non-gait actions.