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ThinkMind // ACHI 2017, The Tenth International Conference on Advances in Computer-Human Interactions // View article achi_2017_8_30_20186


Human Activities Recognition in Smart Homes Using Stacked Autoencoders

Authors:
Nour El Houda Mbarki
Ridha Ejbali
Mourad Zaied

Keywords: smart home; recognition of human activities; deep learning; stacked auto-encoders

Abstract:
There is a growing interest in the domain of smart homes. One of the most important tasks in this domain is the recognition of inhabitants’ activities. To ameliorate the proposed approaches, we propose, in this paper, a Staked Autoencoder (SAE) algorithm based on a deep learning framework for recognizing activities in a smart home. Our approach is tested on the Washington State University (WSU) dataset. We will show that our proposed approach outperforms existing methods such as the Artificial Neural Networks (ANNs) in terms of recognition accuracy of activities. In particular, the SAE shows an accuracy of 87.5% in recognizing activities based on WSU smart home dataset while the ANN algorithm has shown an accuracy of 79.5% on the same dataset.

Pages: 176 to 180

Copyright: Copyright (c) IARIA, 2017

Publication date: March 19, 2017

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-538-8

Location: Nice, France

Dates: from March 19, 2017 to March 23, 2017

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