  Parkinson's disease is a neuro-degenerative disorder affecting tens of
millions of people worldwide. Lately, there has been considerable interest in
systems for at-home monitoring of patients, using wearable devices which
contain inertial measurement units. We present a new wavelet-based approach for
analysis of data from single wrist-worn smart-watches, and show high detection
performance for tremor, bradykinesia, and dyskinesia, which have been the major
targets for monitoring in this context. We also discuss the implication of our
controlled-experiment results for uncontrolled home monitoring of freely
behaving patients.
