Detection of motor impairment in Parkinson's disease via mobile touchscreen typing

Arroyo Gallego, Teresa and Ledesma Carbayo, Maria Jesus and Sánchez Ferro, Álvaro and Butterworth, Ian and Mendoza, Carlos S. and Matarazzo, Michele and Montero Escribano, Paloma and López Blanco, Roberto and Puertas Martín, Verónica and Trincado Soriano, Rocío and Giancardo, Luca (2017). Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. "IEEE Transactions on Biomedical engineering", v. 64 (n. 9); pp. 1994-2002. ISSN 0018-9294. https://doi.org/10.1109/TBME.2017.2664802.

Description

Title: Detection of motor impairment in Parkinson's disease via mobile touchscreen typing
Author/s:
  • Arroyo Gallego, Teresa
  • Ledesma Carbayo, Maria Jesus
  • Sánchez Ferro, Álvaro
  • Butterworth, Ian
  • Mendoza, Carlos S.
  • Matarazzo, Michele
  • Montero Escribano, Paloma
  • López Blanco, Roberto
  • Puertas Martín, Verónica
  • Trincado Soriano, Rocío
  • Giancardo, Luca
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Biomedical engineering
Date: 18 August 2017
ISSN: 0018-9294
Volume: 64
Subjects:
Freetext Keywords: Feature extraction, finger tapping, keystroke dynamics, mHealth, passive monitoring, signal processing, smartphone
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7291820MVISIONFundacion para el Conocimiento MADRIMASDMVISION

More information

Item ID: 50705
DC Identifier: http://oa.upm.es/50705/
OAI Identifier: oai:oa.upm.es:50705
DOI: 10.1109/TBME.2017.2664802
Official URL: https://ieeexplore.ieee.org/document/7859354/
Deposited by: Memoria Investigacion
Deposited on: 13 May 2018 07:42
Last Modified: 30 Apr 2019 11:39
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