Real World Parkinson's Disease Tremor and Score Prediction using Wearable IMU Sensors

Parkinson's Disease (PD) is the second most common neurodegenerative disease and prevalence is increasing as populations age. However, because it remains difficult to sensitively evaluate symptom progression, testing of therapies which change the disease course has been hampered (and no such therapy is yet licensed). The more recent use of home monitoring systems provides the potential for providing granular assessment of symptom progression, specifically, tremor evaluation, by continuous monitoring using inertial measurement units. A significant barrier remains in obtaining the ground truth information to assess inference models in these non-laboratory environments. Given these limitations, in this paper we propose a machine learning tremor regression model and classifier heuristic that uses minimal annotations. We use a real-world dataset of 12 participants, where pairs of participants, one with PD and a healthy control, are monitored over a four-day period in a home environment with wrist-worn devices. Our approach leverages the accelerometer frequency-time representation. We evaluate the classifier heuristic on the control participants. The tremor score regression model is trained on the self-assessments of the participants. We show that the tremor classifier can achieve false positive rates (FPR) less than 0.001 (on average one false positive every five hours) on control participants. For a particular participant, we show that the machine learning regression model achieves a mean absolute error (MAE) of 0.46 as compared to a polynomial regression model (degree=3) with MAE of 0.99. This research is the first to provide a semi-supervised machine learning approach using self-report of symptoms to continually predict tremor scores for individuals with PD.