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We introduce Walk4Me, a telehealth community mobility assessment systemdesigned to facilitate early diagnosis, severity, and progressionidentification. Our system achieves this by 1) enabling early diagnosis, 2)identifying early indicators of clinical severity, and 3) quantifying andtracking the progression of the disease across the ambulatory phase of thedisease. To accomplish this, we employ an Artificial Intelligence (AI)-baseddetection of gait characteristics in patients and typically developing peers.Our system remotely and in real-time collects data from device sensors (e.g.,acceleration from a mobile device, etc.) using our novel Walk4Me API. Our webapplication extracts temporal/spatial gait characteristics and raw data signalcharacteristics and then employs traditional machine learning and deep learningtechniques to identify patterns that can 1) identify patients with gaitdisturbances associated with disease, 2) describe the degree of mobilitylimitation, and 3) identify characteristics that change over time with diseaseprogression. We have identified several machine learning techniques thatdifferentiate between patients and typically-developing subjects with 100%accuracy across the age range studied, and we have also identifiedcorresponding temporal/spatial gait characteristics associated with each group.Our work demonstrates the potential of utilizing the latest advances in mobiledevice and machine learning technology to measure clinical outcomes regardlessof the point of care, inform early clinical diagnosis and treatmentdecision-making, and monitor disease progression.

Walk4Me: Telehealth Community Mobility Assessment, An Automated System for Early Diagnosis and Disease Progression