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Technologies 2017, 5(4), 78; https://doi.org/10.3390/technologies5040078

A Stochastic Memory Model for ADL Detection in Human Households

Department of Computer Science, University of Technology Dresden, 01062 Dresden, Germany
This paper is an extended version of our paper published in the 10th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), Island of Rhodes, Greece, 21–23 June 2017.
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Received: 16 October 2017 / Revised: 17 November 2017 / Accepted: 24 November 2017 / Published: 30 November 2017
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Abstract

Many Human Activity Recognition (HAR) systems are able to detect sequential executed Activity of Daily Living (ADL). However, a person is capable of doing two things in parallel or pausing one ADL and finishing it later. Thus, a HAR system must be capable of remembering and deciding which ADL is completed and which might be continued after the current ADL. We address this case by combining a stochastic Markov model and a psychological memory function to detect parallel ADL. For the evaluation, we use an input dataset and a publicly available benchmark. Our approach outperforms the leading HAR systems for the used benchmark by 5%, while using a more cost-effective installation environment. Furthermore, we address an unsupervised learning method to train the HAR system and explain the algorithm of parallel ADL detection in detail. View Full-Text
Keywords: Markov model; ADL; human activity detection; cognitive model; Ambient Assisted Living (AAL); human behavior; HAR Markov model; ADL; human activity detection; cognitive model; Ambient Assisted Living (AAL); human behavior; HAR
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Clement, J.; Kabitzsch, K. A Stochastic Memory Model for ADL Detection in Human Households. Technologies 2017, 5, 78.

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