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IEEE Trans Biomed Eng. 2017 Sep;64(9):1994-2002. doi: 10.1109/TBME.2017.2664802. Epub 2017 Feb 20.

Detection of Motor Impairment in Parkinson's Disease Via Mobile Touchscreen Typing.

Author information

1
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
2
Biomedical Image TechnologiesUniversidad Politécnica de Madrid.
3
Madrid-MIT M+Visión Consortium, Research Laboratory of ElectronicsMassachusetts Institute of Technology.
4
Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
5
Asana Weartech, Spain and also with Madrid-MIT M+Visión Consortium, Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
6
HM Hospitales-Centro Integral en Neurociencias HM CINAC, Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain.
7
Movement Disorders Unit, Hospital Clinico San Carlos, Madrid, Spain.
8
Instituto de Investigación Hospital 12 de Octubre (i+12), Madrid, Spain.
9
Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Abstract

Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and progression of the disease. Mobile technology can help clinical decision making by completing the information of motor status between hospital visits. This paper presents an algorithm to detect PD by analyzing the typing activity on smartphones independently of the content of the typed text. We propose a set of touchscreen typing features based on a covariance, skewness, and kurtosis analysis of the timing information of the data to capture PD motor signs. We tested these features, both independently and in a multivariate framework, in a population of 21 PD and 23 control subjects, achieving a sensitivity/specificity of 0.81/0.81 for the best performing feature and 0.73/0.84 for the best multivariate method. The results of the alternating finger-tapping, an established motor test, measured in our cohort are 0.75/0.78. This paper contributes to the development of a home-based, high-compliance, and high-frequency PD motor test by analysis of routine typing on touchscreens.

PMID:
28237917
DOI:
10.1109/TBME.2017.2664802
[Indexed for MEDLINE]

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