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

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 y 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.

Descripción

Título: Detection of motor impairment in Parkinson's disease via mobile touchscreen typing
Autor/es:
  • 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
Tipo de Documento: Artículo
Título de Revista/Publicación: IEEE Transactions on Biomedical engineering
Fecha: 18 Agosto 2017
Volumen: 64
Materias:
Palabras Clave Informales: Feature extraction, finger tapping, keystroke dynamics, mHealth, passive monitoring, signal processing, smartphone
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 50705
Identificador DC: http://oa.upm.es/50705/
Identificador OAI: oai:oa.upm.es:50705
Identificador DOI: 10.1109/TBME.2017.2664802
URL Oficial: https://ieeexplore.ieee.org/document/7859354/
Depositado por: Memoria Investigacion
Depositado el: 13 May 2018 07:42
Ultima Modificación: 13 May 2018 07:42
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