Abstract: Systems and methods for diagnosing peripheral artery disease (PAD) in a patient. The method includes training a machine learning model with gait characteristic data extracted from acceleration data for patients known to have PAD and patients that do not have PAD, receiving acceleration data related to a specific patient, extracting gait characteristic data from the acceleration data related to the specific patient, feeding the extracted gait characteristic data to the trained machine learning model to identify one or more gait features for the specific patient, and diagnosing the specific patient as having PAD or not having PAD based on the one or more identified gait features. The machine learning model can also be trained using biomechanics data extracted using a digital camera, e.g., a high-speed digital camera motion capture system.