Multi-resident activity recognition using label combination approach in smart home environment
Activity recognition in smart home environment is becoming challenging when it is involving more than one resident living in the same space. It is not merely recognizing the activity performed nevertheless to track and identify the performer of specific activity also need to address in order to prov...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/64644/ http://psasir.upm.edu.my/id/eprint/64644/1/Multi-resident%20activity%20recognition%20using%20label%20combination%20approach%20in%20smart%20home%20environment.pdf |
| _version_ | 1848855057361010688 |
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| author | Mohamed, Raihani Perumal, Thinagaran Sulaiman, Md. Nasir Mustapha, Norwati |
| author_facet | Mohamed, Raihani Perumal, Thinagaran Sulaiman, Md. Nasir Mustapha, Norwati |
| author_sort | Mohamed, Raihani |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Activity recognition in smart home environment is becoming challenging when it is involving more than one resident living in the same space. It is not merely recognizing the activity performed nevertheless to track and identify the performer of specific activity also need to address in order to provide the great autonomous for ambient intelligence system (AmI). It is a challenging task due to diversity and complexity of sensor fusion that only using the binary data from single type technology of ambient sensors. Strong approach is needed to identify types of activities performed at the same time to track which resident are performing that particular activity. Previously, researchers build the multi-resident activity model regardless the performer, thus the data association also fails to tackle the problem applicably. This research presents the multi-label classification approach to recognize the activity at the same is able to track the resident in multi-resident in a smart home setting. It has been tested on the real smart home datasets using Label Combination method of multi-label classification technique using random forest as its base classifier. The Hamming score, accuracy and exact match are selected as evaluation metrics to measure the proposed solution. |
| first_indexed | 2025-11-15T11:19:42Z |
| format | Conference or Workshop Item |
| id | upm-64644 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:19:42Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-646442018-08-13T03:16:25Z http://psasir.upm.edu.my/id/eprint/64644/ Multi-resident activity recognition using label combination approach in smart home environment Mohamed, Raihani Perumal, Thinagaran Sulaiman, Md. Nasir Mustapha, Norwati Activity recognition in smart home environment is becoming challenging when it is involving more than one resident living in the same space. It is not merely recognizing the activity performed nevertheless to track and identify the performer of specific activity also need to address in order to provide the great autonomous for ambient intelligence system (AmI). It is a challenging task due to diversity and complexity of sensor fusion that only using the binary data from single type technology of ambient sensors. Strong approach is needed to identify types of activities performed at the same time to track which resident are performing that particular activity. Previously, researchers build the multi-resident activity model regardless the performer, thus the data association also fails to tackle the problem applicably. This research presents the multi-label classification approach to recognize the activity at the same is able to track the resident in multi-resident in a smart home setting. It has been tested on the real smart home datasets using Label Combination method of multi-label classification technique using random forest as its base classifier. The Hamming score, accuracy and exact match are selected as evaluation metrics to measure the proposed solution. IEEE 2017 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/64644/1/Multi-resident%20activity%20recognition%20using%20label%20combination%20approach%20in%20smart%20home%20environment.pdf Mohamed, Raihani and Perumal, Thinagaran and Sulaiman, Md. Nasir and Mustapha, Norwati (2017) Multi-resident activity recognition using label combination approach in smart home environment. In: 2017 IEEE 21st International Symposium on Electronics (ISCE 2017), 14-15 Nov. 2017, Universiti Putra Malaysia. (pp. 69-71). 10.1109/ISCE.2017.8355551 |
| spellingShingle | Mohamed, Raihani Perumal, Thinagaran Sulaiman, Md. Nasir Mustapha, Norwati Multi-resident activity recognition using label combination approach in smart home environment |
| title | Multi-resident activity recognition using label combination approach in smart home environment |
| title_full | Multi-resident activity recognition using label combination approach in smart home environment |
| title_fullStr | Multi-resident activity recognition using label combination approach in smart home environment |
| title_full_unstemmed | Multi-resident activity recognition using label combination approach in smart home environment |
| title_short | Multi-resident activity recognition using label combination approach in smart home environment |
| title_sort | multi-resident activity recognition using label combination approach in smart home environment |
| url | http://psasir.upm.edu.my/id/eprint/64644/ http://psasir.upm.edu.my/id/eprint/64644/ http://psasir.upm.edu.my/id/eprint/64644/1/Multi-resident%20activity%20recognition%20using%20label%20combination%20approach%20in%20smart%20home%20environment.pdf |