Deep learning for multi-resident activity recognition in ambient sensing smart homes
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 challen...
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2019
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/78083/ http://psasir.upm.edu.my/id/eprint/78083/1/Deep%20learning%20for%20multi-resident%20activity%20recognition%20in%20ambient%20sensing%20smart%20homes.pdf |
| _version_ | 1848858408632975360 |
|---|---|
| author | Natani, Anubhav Sharma, Abhishek Perumal, Thinagaran Suman, Sukhavasi |
| author_facet | Natani, Anubhav Sharma, Abhishek Perumal, Thinagaran Suman, Sukhavasi |
| author_sort | Natani, Anubhav |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T12:12:58Z |
| format | Conference or Workshop Item |
| id | upm-78083 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:12:58Z |
| publishDate | 2019 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-780832020-06-03T06:30:20Z http://psasir.upm.edu.my/id/eprint/78083/ Deep learning for multi-resident activity recognition in ambient sensing smart homes Natani, Anubhav Sharma, Abhishek Perumal, Thinagaran Suman, Sukhavasi 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. IEEE 2019 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/78083/1/Deep%20learning%20for%20multi-resident%20activity%20recognition%20in%20ambient%20sensing%20smart%20homes.pdf 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). 10.1109/GCCE46687.2019.9015212 |
| spellingShingle | Natani, Anubhav Sharma, Abhishek Perumal, Thinagaran Suman, Sukhavasi Deep learning for multi-resident activity recognition in ambient sensing smart homes |
| title | Deep learning for multi-resident activity recognition in ambient sensing smart homes |
| title_full | Deep learning for multi-resident activity recognition in ambient sensing smart homes |
| title_fullStr | Deep learning for multi-resident activity recognition in ambient sensing smart homes |
| title_full_unstemmed | Deep learning for multi-resident activity recognition in ambient sensing smart homes |
| title_short | Deep learning for multi-resident activity recognition in ambient sensing smart homes |
| title_sort | deep learning for multi-resident activity recognition in ambient sensing smart homes |
| url | http://psasir.upm.edu.my/id/eprint/78083/ http://psasir.upm.edu.my/id/eprint/78083/ http://psasir.upm.edu.my/id/eprint/78083/1/Deep%20learning%20for%20multi-resident%20activity%20recognition%20in%20ambient%20sensing%20smart%20homes.pdf |