期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 64, 期 9, 页码 1994-2002出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2017.2664802
关键词
Feature extraction; finger tapping; keystroke dynamics; mHealth; passive monitoring; signal processing; smartphone
资金
- Comunidad de Madrid
- Fundacion Ramon Areces
- Consejeria de Educacion, Juventud y Deporte of Comunidad de Madrid
- People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7) under REA Grant [291820]
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 highfrequency PD motor test by analysis of routine typing on touchscreens.
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