Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures


For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: This is the peer reviewed version of the following article:Kubota, K. J., Chen, J. A., & Little, M. A. (2016). Machine learning for large-scale wearable sensor data in Parkinson disease: concepts, promises, pitfalls, and futures. Movement Disorders, 31(9), 1314-1326, which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Uncontrolled Keywords: machine learning,artificial intelligence,data sciences,wearables,digital sensors,Medicine(all),Neurology,Clinical Neurology
Publication ISSN: 1531-8257
Last Modified: 03 Jun 2024 07:21
Date Deposited: 23 May 2016 12:55
Full Text Link: http://onlineli ... .26693/abstract
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-09
Published Online Date: 2016-08-08
Accepted Date: 2016-05-10
Submitted Date: 2016-02-27
Authors: Kubota, Ken J.
Chen, Jason A.
Little, Max A. (ORCID Profile 0000-0002-1507-3822)



Version: Accepted Version

License: ["licenses_description_unspecified" not defined]

| Preview

Export / Share Citation


Additional statistics for this record