Affiliations: [a] Department of Computer Science, University of Rostock, Rostock, Germany | [b] German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany | [c] Department of Psychiatry, University of Rostock, Rostock, Germany | [d] Department of Psychiatry, KMG Kliniken, Güstrow, Germany
Correspondence:
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Correspondence to: Thomas Kirste, Department of Computer Science, University of Rostock, 18051 Rostock, Germany. Tel.: +49 381 498 7510; Fax: +49 381 498 7412; E-mail: [email protected].
Abstract: Background:Early detection of behavioral changes in Alzheimer’s disease (AD) would help the design and implementation of specific interventions. Objective:The target of our investigation was to establish a correlation between diagnosis and unconstrained motion behavior in subjects without major clinical behavior impairments. Method:We studied everyday motion behavior in 23 dyads with one partner suffering from AD dementia and one cognitively healthy partner in the subjects’ home, employing ankle-mounted three-axes accelerometric sensors. We determined frequency features obtained from the signal envelopes computed by an envelope detector for the carrier band 0.5 Hz to 5 Hz. Based on these features, we employed quadratic discriminant analysis for building models discriminating between AD patients and healthy controls. Results:After leave-one-out cross-validation, the classification accuracy of motion features reached 91% and was superior to the classification accuracy based on the Cohen-Mansfield Agitation Inventory (CMAI). Motion features were significantly correlated with MMSE and CMAI scores. Conclusion:Our findings suggest that changes of everyday behavior are detectable in accelerometric behavior protocols even in the absence of major clinical behavioral impairments in AD.
