A Stochastic Memory Model for ADL Detection in Human Households†
AbstractMany 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
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Clement, J.; Kabitzsch, K. A Stochastic Memory Model for ADL Detection in Human Households. Technologies 2017, 5, 78.
Clement J, Kabitzsch K. A Stochastic Memory Model for ADL Detection in Human Households. Technologies. 2017; 5(4):78.Chicago/Turabian Style
Clement, Jana; Kabitzsch, Klaus. 2017. "A Stochastic Memory Model for ADL Detection in Human Households." Technologies 5, no. 4: 78.
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