  We use autoregressive hidden Markov models and a time-frequency approach to
create meaningful quantitative descriptions of behavioral characteristics of
cerebellar ataxias from wearable inertial sensor data gathered during movement.
Wearable sensor data is relatively easily collected and provides direct
measurements of movement that can be used to develop useful behavioral
biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative
diseases are critical to supporting early detection, drug development efforts,
and targeted treatments. We create a flexible and descriptive set of features
derived from accelerometer and gyroscope data collected from wearable sensors
while participants perform clinical assessment tasks, and with them estimate
disease status and severity. A short period of data collection ($<$ 5 minutes)
yields enough information to effectively separate patients with ataxia from
healthy controls with very high accuracy, to separate ataxia from other
neurodegenerative diseases such as Parkinson's disease, and to give estimates
of disease severity.
