SCH: EXP: Monitoring Motor Symptoms in Parkinson's Disease with Wearable Devices

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Parkinson's Disease (PD) poses a serious threat to the elderly population, affecting as many as one million Americans. There is no cure, and medications can only provide symptomatic relief. In addition, costs associated with PD, including treatment, social security payments, and lost income from inability to work, is estimated to be nearly $25 billion per year in the United States alone. The current state-of-the-art in PD management suffers from several shortcomings: (1) frequent clinic visits are a major contributor to the high cost of PD treatment and are inconvenient for the patient, especially in a population for which traveling is difficult; (2) inaccurate patient self-reports and 15-20 minute clinic visits are not enough information for doctors to accurately assess their patients, leading to difficulties in monitoring patient symptoms and medication response; and (3) motor function assessments are subjective, making it difficult to monitor disease progression. Furthermore, because they must be performed by a trained clinician, it is infeasible to do frequent motor function assessments. This project aims to promote a paradigm shift in PD management through in-home monitoring using wearable accelerometers and machine learning. Novel algorithms and experimental protocols are developed to allow for robust detection and assessment of PD motor symptoms during daily living environments. Specifically, this project develops algorithms for weakly-supervised learning, time series analysis, and personalization of classifiers. In previous studies, data was collected in controlled environments for a short amount of time (1-4 hours) and manually labeled for fully-supervised learning. In contrast, this project collects long-term (several weeks), in-home data where the participants' actions are natural and unscripted. Participants use a cell phone app to label their own data, marking segments of time as containing or not containing the occurrence of a PD motor symptom. Since the exact time of the symptom is unknown, this constitutes weakly-labeled data. This project extends multiple-instance learning algorithms for learning from weakly-labeled data in time series. Additional major technical challenges include detection of subtle motor symptoms and local minima during optimization. To further increase robustness and generalization, this project explores the use of personalization algorithms to learn person-specific models of motor symptoms from unsupervised data. The proposed techniques for weakly-supervised learning and personalization are general, and they can be applied to other human sensing problems.