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Unsupervised statistical concept drift detection for behaviour abnormality detection

Unsupervised statistical concept drift detection for behaviour abnormality detection

Friedrich, Björn; Sawabe, Taishi; Hein, Andreas
URI: http://hdl.handle.net/10061/14905
Date: 2022-05-10

Abstract:

Abnormal behaviour can be an indicator for a medical condition in older adults. Our novel unsupervised statistical concept drift detection approach uses variational autoencoders for estimating the parameters for a statistical hypothesis test for abnormal days. As feature, the Kullback–Leibler divergence of activity probability maps derived from power and motion sensors were used. We showed the general feasibility (min. F1-Score of 91 %) on an artificial dataset of four concept drift types. Then we applied our new method to our real–world dataset collected from the homes of 20 (pre–)frail older adults (avg. age 84.75 y). Our method was able to find abnormal days when a participant suffered from severe medical condition.

Information:

 
Version: none
Journal title: Applied Intelligence
Vol(Issue): 53 (3)
Page: 2527-2537
Language: en
Original DOI: https://doi.org/10.1007/s10489-022-03611-3
Copyright: © The Author(s) 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Access to original: https://link.springer.com/article/10.1007/s10489-022-03611-3

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