Gait Signatures in Patients with Peripheral Artery Disease

Machine learning is used to improve treatment of functional problems in patients with peripheral artery disease (PAD). Previously collected biomechanics data is used to identify representative gait signatures of PAD to 1) determine the gait signatures of patients with PAD and 2) the ability of limb acceleration measurements to identify and model the meaningful biomechanics measures from PAD data.