Jun 9, 2020 · In this paper we investigate the dependencies of the activities from residents and their interaction with the environments.
Such models are the keystone for the future of smart homes where occupants can be assisted with non-intrusive technologies. Much attention has been put on this ...
Title: Mixed-dependency models for multi-resident activity recognition in smart homes. ; Language: English ; Authors: Tran, Son N. · (AUTHOR) [email protected]
Such models are the keystone for the future of smart homes where occupants can be assisted with non-intrusive technologies. Much attention has been put on this ...
This paper explores the effectiveness and efficiency of temporal learning algorithms using sequential data and non-temporalLearning algorithms using ...
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The third step involves selecting and training a machine learning model, taking into account the number of activities and the quantity of data available [7].
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In this paper, we have applied deep learning algorithms on the real world ARAS Multi Resident data set, which consists of data from two houses, each with two ...
Tran et al. Mixed-dependency models for multi-resident activity recognition in smart-homes. ACM Trans. Web. (2010).
Mixed-dependency models for multi-resident activity recognition in smart homes. Article 09 June 2020. Activity Recognition in Smart Homes. Article 29 November ...
ABSTRACT Several methods have been proposed in the last two decades to recognize human activities based on sensor data acquired in smart-homes.
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