Using wearable sensor-based technologies to detect changes in health status for prevention of adverse health events and to improve overall quality of life

The project goal is to determine the clinical utility of Orpyx LogR technology to detect gait changes and their efficacy to predict and monitor fall risk. Project I will use existing data to determine sensitivity and specificity for prospective classification of fallers and non-fallers for a composite measure drawn from an extensive battery including single and/or dual-task IMU-derived gait metrics as well as from force plate gait initiation data. Respectively, Project II and III will concurrently provide validation of Orpyx LogR technology measurements and then determine sensitivity and specificity for retrospective and prospective classification of fallers and non-fallers for a composite measure drawn from a battery including clinical tests of dynamic balance and Orpyx LogR derived measures including postural sway during quiet stance, as well as gait measures during gait initiation and single and/or dual task walking. Project IV will use will use custom algorithms to develop client-specific models of fall prediction incorporating relevant measures identified in Project II. These measurements of gait and balance can act as biomarkers to provide early detection of changes in health status. Providing timely information to caregivers about changes in health status will allow for appropriate interventions with potential to mitigate adverse health events.

Intern: 
Drew Commandeur
Faculty Supervisor: 
Marc Klimstra
Province: 
British Columbia
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