MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition
Smart home automation is protective and preventive measures that are taken to monitor elderly people in a non-intrusive manner using simple and pervasive sensors termed Ambient Assistive Living. The smart home produces a large volume of sensor activations to predict an elder’s health status to impro...
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| Format: | Article |
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Springer
2022
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| _version_ | 1796984181753905152 |
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| author | Ramanujam, E. Perumal, Thinagaran |
| author_facet | Ramanujam, E. Perumal, Thinagaran |
| author_sort | Ramanujam, E. |
| collection | UPM |
| description | Smart home automation is protective and preventive measures that are taken to monitor elderly people in a non-intrusive manner using simple and pervasive sensors termed Ambient Assistive Living. The smart home produces a large volume of sensor activations to predict an elder’s health status to improve the quality of life and independent living. Machine learning techniques are very familiar and popular in recognizing single resident activities using such a sensor reading. However, multi-resident activities are more complex, and no correlation exists between sensor readings and activities. Recently deep learning and graphical models have been proposed to solve this problem, but it consumes more time to train the model. Moreover, models are primarily executed in parallel, or multi-resident activity recognition has been converted to single for classifying events. This paper proposes a Multi-label Multi-output Hybrid Sequential Model (MLMO-HSM), a feature engineering approach with a hybrid sequential model to recognize the multi-resident activities concurrently in the shortest time. Experimentation has been performed with various machine learning, graphical and deep learning models at the different sizes of the ARAS dataset to validate the efficiency of the proposed model in terms of activity recognition and computation time. |
| first_indexed | 2024-03-06T11:16:50Z |
| format | Article |
| id | upm.eprints-102191 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:16:50Z |
| publishDate | 2022 |
| publisher | Springer |
| record_format | dspace |
| spelling | upm.eprints-1021912023-07-10T00:19:14Z http://psasir.upm.edu.my/id/eprint/102191/ MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition Ramanujam, E. Perumal, Thinagaran Smart home automation is protective and preventive measures that are taken to monitor elderly people in a non-intrusive manner using simple and pervasive sensors termed Ambient Assistive Living. The smart home produces a large volume of sensor activations to predict an elder’s health status to improve the quality of life and independent living. Machine learning techniques are very familiar and popular in recognizing single resident activities using such a sensor reading. However, multi-resident activities are more complex, and no correlation exists between sensor readings and activities. Recently deep learning and graphical models have been proposed to solve this problem, but it consumes more time to train the model. Moreover, models are primarily executed in parallel, or multi-resident activity recognition has been converted to single for classifying events. This paper proposes a Multi-label Multi-output Hybrid Sequential Model (MLMO-HSM), a feature engineering approach with a hybrid sequential model to recognize the multi-resident activities concurrently in the shortest time. Experimentation has been performed with various machine learning, graphical and deep learning models at the different sizes of the ARAS dataset to validate the efficiency of the proposed model in terms of activity recognition and computation time. Springer 2022-12-11 Article PeerReviewed Ramanujam, E. and Perumal, Thinagaran (2022) MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition. Journal of Ambient Intelligence and Humanized Computing, 14 (3). pp. 2313-2325. ISSN 1868-5137; ESSN: 1868-5145 https://link.springer.com/article/10.1007/s12652-022-04487-4#citeas 10.1007/s12652-022-04487-4 |
| spellingShingle | Ramanujam, E. Perumal, Thinagaran MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition |
| title | MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition |
| title_full | MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition |
| title_fullStr | MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition |
| title_full_unstemmed | MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition |
| title_short | MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition |
| title_sort | mlmo hsm multi label multi output hybrid sequential model for multi resident smart home activity recognition |
| work_keys_str_mv | AT ramanujame mlmohsmmultilabelmultioutputhybridsequentialmodelformultiresidentsmarthomeactivityrecognition AT perumalthinagaran mlmohsmmultilabelmultioutputhybridsequentialmodelformultiresidentsmarthomeactivityrecognition |