Deep learning for multi-resident activity recognition in ambient sensing smart homes

Citation

Natani, Anubhav and Sharma, Abhishek and Perumal, Thinagaran and Suman, Sukhavasi (2019) Deep learning for multi-resident activity recognition in ambient sensing smart homes. In: 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), 15-18 Oct. 2019, Osaka, Japan. (pp. 340-341).

Abstract

Advances in smart home technology and IoT devices has enabled us for monitoring of human activities for their health status and efficient energy consumption. Machine learning has been a great tool for the prediction of human activities. However, Multi-resident activity recognition is still a challenge as there is no direct correlation between sensor values and resident activities. In this paper, we have displayed the state of art deep learning algorithms on the real-world ARAS multi-resident dataset, which consists of data from two houses each with two residents. We have used different variations of RNN on the dataset and measured their performance with fewer data and more data and with data generated with GAN.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Computer Science and Information Technology
DOI Number:	https://doi.org/10.1109/GCCE46687.2019.9015212
Publisher:	IEEE
Keywords:	Activities of daily life (ADL); Multi resident; Smart home; GANs; Sequential networks; Deep learning; Human activity recognition
Depositing User:	Nabilah Mustapa
Date Deposited:	03 Jun 2020 06:30
Last Modified:	03 Jun 2020 06:30
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/GCCE46687.2019.9015212
URI:	http://psasir.upm.edu.my/id/eprint/78083
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