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One-Class Classification Approach in Accelerometer-Based Remote Monitoring of Physical Activities for Healthcare Applications

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Integrating Artificial Intelligence and IoT for Advanced Health Informatics

Part of the book series: Internet of Things ((ITTCC))

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Abstract

Human activity recognition (HAR) enables numerous application scenarios in ambient assisted living thanks to IoT integration. Healthcare is one of the most prominent use cases of HAR serving individuals who suffer from aging or disabilities as well as healthy people. Remote monitoring of daily activities within home environment may offer assistance in tracking adherence of patients to therapeutical procedures such as exercise monitoring. Within an IoT framework, the type of sensors used influences usability of the HAR system. Accelerometers introduce a noninvasive sensing option as opposed to cameras which intrude into users’ privacy. Recognition of target activities when they are performed among other activities brings about challenges. Typical multi-class classification employed in such recognition tasks necessitates training data collection for all activity types which can be encountered in the prediction stage. Due to unlimited variety of daily living activities, the number of activity classes for which training data should be collected is infinitely many. Expressing the recognition problem in terms of one-class classification (OCC) architecture can aid in resolving this bottleneck. In this chapter, we propose an OCC-based HAR architecture with IoT integration. In our OCC scheme, we utilize artificial data generation (ADG) to generate training data for the negative class based on the target class. In the proposed model, the target class is the only class for which training data are collected. The OCC scheme enables recognizing the target class when the other class is represented with artificially generated data. We present the results of an experimental study for our OCC model on a dataset consisting of ambulatory and static activities.

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Authors and Affiliations

  1. Department of Computer Engineering, Yeditepe University, Atasehir, İstanbul, Turkey

    Gamze Uslu, Berk Unal, Aylin Aydın & Sebnem Baydere

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  1. Gamze Uslu
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Correspondence to Gamze Uslu .

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  1. ICAR-CNR, University of Calabria, Rende, Italy

    Carmela Comito

  2. ICAR-CNR, University of Calabria, Rende, Italy

    Agostino Forestiero

  3. DIMES, University of Calabria, Rende, Italy

    Ester Zumpano

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Uslu, G., Unal, B., Aydın, A., Baydere, S. (2022). One-Class Classification Approach in Accelerometer-Based Remote Monitoring of Physical Activities for Healthcare Applications. In: Comito, C., Forestiero, A., Zumpano, E. (eds) Integrating Artificial Intelligence and IoT for Advanced Health Informatics. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-91181-2_2

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