Detecting and tracking gait asymmetries with wearable accelerometers

Abstract

Gait asymmetry can be a useful indicator of a variety of medical and pathological conditions, including musculoskeletal injury (MSI), neurological damage associated with stroke or head trauma, and a variety of age-related disorders. Body-worn accelerometers can enable real-time monitoring and detection of changes in gait asymmetry, thereby informing medical conditions and triggering timely interventions. We propose a practical and robust algorithm for detecting gait asymmetry based on summary statistics extracted from accelerometers attached to each foot. By registering simultaneous acceleration differences between the two feet, these asymmetry features provide robustness to a variety of confounding factors, such as changes in walking speed and load carriage. Evaluating the algorithm on natural walking data with induced gait asymmetries, we demonstrate that the extracted features are sensitive to the sign and magnitude of gait asymmetries and enable the detection and tracking of asymmetries during continuous monitoring.