  We introduce Walk4Me, a telehealth community mobility assessment system
designed to facilitate early diagnosis, severity, and progression
identification. Our system achieves this by 1) enabling early diagnosis, 2)
identifying early indicators of clinical severity, and 3) quantifying and
tracking the progression of the disease across the ambulatory phase of the
disease. To accomplish this, we employ an Artificial Intelligence (AI)-based
detection 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 web
application extracts temporal/spatial gait characteristics and raw data signal
characteristics and then employs traditional machine learning and deep learning
techniques to identify patterns that can 1) identify patients with gait
disturbances associated with disease, 2) describe the degree of mobility
limitation, and 3) identify characteristics that change over time with disease
progression. We have identified several machine learning techniques that
differentiate between patients and typically-developing subjects with 100%
accuracy across the age range studied, and we have also identified
corresponding temporal/spatial gait characteristics associated with each group.
Our work demonstrates the potential of utilizing the latest advances in mobile
device and machine learning technology to measure clinical outcomes regardless
of the point of care, inform early clinical diagnosis and treatment
decision-making, and monitor disease progression.
