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Sensors 2017, 17(7), 1679;

Classification of Alzheimer’s Patients through Ubiquitous Computing

Department of Mathematics, Statistics and Computer Science, Universidad de Cantabria, 39005 Santander, Spain
Cognitive Disorders Unit, Department of Neurology, Marqués de Valdecilla University Hospital (HUMV) Valdecilla Biomedical Research Institute (IDIVAL), 39008 Santander, Spain
This paper is an extended version of our paper published in Duque, R.; Nieto-Reyes, A.; Martínez, C.; Montaña, J.L. Detecting Human Movement Patterns Through Data Provided by Accelerometers. A Case Study Regarding Alzheimer’s Disease, In Proceedings of the Ubiquitous Computing and Ambient Intelligence. San Bartolomé de Tirajana, Spain, 29 November–2 December 2016; 10069, pp. 56–66.
Author to whom correspondence should be addressed.
Received: 20 May 2017 / Revised: 13 July 2017 / Accepted: 18 July 2017 / Published: 21 July 2017
(This article belongs to the Special Issue Selected Papers from UCAmI 2016)
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Functional data analysis and artificial neural networks are the building blocks of the proposed methodology that distinguishes the movement patterns among c’s patients on different stages of the disease and classifies new patients to their appropriate stage of the disease. The movement patterns are obtained by the accelerometer device of android smartphones that the patients carry while moving freely. The proposed methodology is relevant in that it is flexible on the type of data to which it is applied. To exemplify that, it is analyzed a novel real three-dimensional functional dataset where each datum is observed in a different time domain. Not only is it observed on a difference frequency but also the domain of each datum has different length. The obtained classification success rate of 83 % indicates the potential of the proposed methodology. View Full-Text
Keywords: Alzheimer; functional data analysis; healthcare; hypothesis testing; pattern recognition; supervised classification; ubiquitous computing Alzheimer; functional data analysis; healthcare; hypothesis testing; pattern recognition; supervised classification; ubiquitous computing

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Nieto-Reyes, A.; Duque, R.; Montaña, J.L.; Lage, C. Classification of Alzheimer’s Patients through Ubiquitous Computing. Sensors 2017, 17, 1679.

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