Measuring Regularity in Daily Behavior for the Purpose of Detecting Alzheimer

Saskia Robben, Ahmed Nait Aicha, Ben Kröse

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
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Abstract

This paper presents a study of sensor data from a person who developed Alzheimer’s disease during a 4-year monitoring period and who is monitored with simple ambient sensors in her home. Our aim is to find data analysis methods that reveal relevant changes in the sensor pattern that occur before the diagnosis. We focus on the quantification of regularity, which is identified as a relevant indicator for the assessment of a disease such as Alzheimer’s. Two unsupervised methods are studied. Restricted Boltzmann Machines are trained and the resulting weights are visualized to see whether there are
changes in regularity in the behavioral pattern. Fast Fourier Transformation is applied to the sensor data and the spectral characteristics are determined and compared with the same purpose. Both methods reveal changes in the pattern between different periods. Both methods therefore are useful in quantifying and understanding changes in the regularity of the daily pattern.
Original languageEnglish
Title of host publicationProceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare
Place of PublicationBrussels
PublisherInstitute for Computer Sciences, Social-Informatics and Telecommunications Engineering
Pages97-100
Number of pages4
ISBN (Print)9781631900518
Publication statusPublished - 2016
EventPervasiveHealth: 10th EAI International Conference on Pervasive Computing Technologies for Healthcare - Cancun, Mexico
Duration: 16 May 201619 May 2016

Conference

ConferencePervasiveHealth
CountryMexico
CityCancun
Period16/05/1619/05/16

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