UC Santa Cruz

Approximately 11 million Americans live with Parkinson’s Disease (PD) or Essential Tremor (ET), and the current standard of care is inadequate. Diagnostic methods rely on qualitative observations rather than quantitative metrics and treatments are often prohibitively expensive or riddled with side effects. This work first develops a non-intrusive wearable device that can perform an automated diagnosis of PD and ET. Next, it develops an algorithm for attitude estimation of the human wrist. Finally, it develops a prototype for a wearable tremor suppression device. The diagnostic device was tested with a group of 30 healthy volunteers who had been educated on the motion characteristics of PD and ET. A 99.9% classification accuracy was achieved during this test. The attitude estimation algorithm was tested with the help of a mechanical test rig that simulated tremor. During a highly dynamic oscillation, the attitude estimate achieved an RMS error of less than 1 degree. This same mechanical test rig was used to evaluate the effectiveness of the tremor suppression prototype. Settings on the rig were adjusted to test tremors of varying amplitude and frequency. The device was able to attenuate tremor amplitude by an average of 60.96% across the configurations tested.