Stealthy Health Sensing to Objectively Characterize Motor Movement Disorders

Hyperkinetic movement disorders affect people in a wide range of ages, from children with autism motor stereotypies to the elderly with Parkinsonian tremor. Unwanted normal and or abnormal movement of the arms significantly affects the quality of life for both young and old. Physicians who manage chronic neurological movement diseases (e.g., Parkinsons) tend to make many decisions based on subjective information without access to objective data that can be difficult to routinely obtain. Assessment information is obtained today by tests characterized as pencil-and-paper tests and from observations that classify behavior of motor response or reaction time. Other assessment approaches rely on technologies such as accelerometers, electromyography, and, more recently, sensors built into smartphones and tablets to obtain test results. In the market today, all smartphone centric solutions still lack both objectivity in the measured data and automatic continuous long-term analysis. In this article, we propose a new smartphone solution that uses stealthy health sensing to more objectively characterize neurological movement disorders using built-in sensors. We evaluate the mobile app by characterizing and monitoring tremor, one of the most common neurological movement disorders. Its objective characterization is important for etiologic consideration and personalized treatment. c © 2013 Published by Elsevier Ltd.