Discovering behavioural predispositions in data to improve human activity recognition

Popko, Maximilian ; Bader, Sebastian ; Lüdtke, Stefan ; Kirste, Thomas

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Document Type: Conference or workshop publication
Year of publication: 2023
Book title: iWOAR '22: Proceedings of the 7th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence : September 19-20, 2022, Rostock, Germany
Page range: 1-7
Conference title: iWOAR 2022
Location of the conference venue: Rostock, Germany
Date of the conference: 19.-20.09.2022
Publisher: Aehnelt, Mario ; Kirste, Thomas
Place of publication: New York, NY, USA
Publishing house: Association for Computing Machinery
ISBN: 978-1-4503-9624-0
Publication language: English
Institution: Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Subject: 004 Computer science, internet
Individual keywords (German): wearable sensors , clustering , human activity recognition, machine learning
Abstract: The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and intra-patient variability. Goal of this paper is to improve the recognition performance by making use of the observation that patients tend to show specific behaviors at certain times of the day or week. We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments. All time segments within a cluster then consist of similar behaviors and thus indicate a behavioral predisposition (BPD). We utilize BPDs by training a classifier for each BPD. Empirically, we demonstrate that when the BPD per time segment is known, activity recognition performance can be substantially improved.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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