Future Activities Prediction Framework in Smart Homes Environment
Smart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL data can be a great asset while analyzing residents’ behavior to provide residents with better and optimized services. A popular example is to analyze residents&am...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9852462/ |
| _version_ | 1811338498419982336 |
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| author | Mai Mohamed Ayman El-Kilany Neamat El-Tazi |
| author_facet | Mai Mohamed Ayman El-Kilany Neamat El-Tazi |
| author_sort | Mai Mohamed |
| collection | DOAJ |
| description | Smart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL data can be a great asset while analyzing residents’ behavior to provide residents with better and optimized services. A popular example is to analyze residents’ behavior to predict their future activities and optimize smart homes performance accordingly. This paper proposes a forecasting framework that utilizes ADL data to predict residents’ next activities in a smart home environment. Forecasting is performed via the conjunction of embedding algorithm to encode the data and Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks to process the data. The proposed framework is evaluated over five real ADL datasets where the experiments show the outperformance of the proposed framework with accuracy scores ranging from 98.7% to 93.8%. |
| first_indexed | 2024-04-13T18:11:45Z |
| format | Article |
| id | doaj.art-f96f410141fb4da5af5dcfe4da399353 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T18:11:45Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f96f410141fb4da5af5dcfe4da3993532022-12-22T02:35:53ZengIEEEIEEE Access2169-35362022-01-0110851548516910.1109/ACCESS.2022.31976189852462Future Activities Prediction Framework in Smart Homes EnvironmentMai Mohamed0https://orcid.org/0000-0001-7794-8738Ayman El-Kilany1Neamat El-Tazi2https://orcid.org/0000-0003-0690-4273Department of Information Systems, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptDepartment of Information Systems, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptDepartment of Information Systems, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptSmart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL data can be a great asset while analyzing residents’ behavior to provide residents with better and optimized services. A popular example is to analyze residents’ behavior to predict their future activities and optimize smart homes performance accordingly. This paper proposes a forecasting framework that utilizes ADL data to predict residents’ next activities in a smart home environment. Forecasting is performed via the conjunction of embedding algorithm to encode the data and Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks to process the data. The proposed framework is evaluated over five real ADL datasets where the experiments show the outperformance of the proposed framework with accuracy scores ranging from 98.7% to 93.8%.https://ieeexplore.ieee.org/document/9852462/Smart homehuman activity recognitionBiLSTM neural networkssequence prediction |
| spellingShingle | Mai Mohamed Ayman El-Kilany Neamat El-Tazi Future Activities Prediction Framework in Smart Homes Environment IEEE Access Smart home human activity recognition BiLSTM neural networks sequence prediction |
| title | Future Activities Prediction Framework in Smart Homes Environment |
| title_full | Future Activities Prediction Framework in Smart Homes Environment |
| title_fullStr | Future Activities Prediction Framework in Smart Homes Environment |
| title_full_unstemmed | Future Activities Prediction Framework in Smart Homes Environment |
| title_short | Future Activities Prediction Framework in Smart Homes Environment |
| title_sort | future activities prediction framework in smart homes environment |
| topic | Smart home human activity recognition BiLSTM neural networks sequence prediction |
| url | https://ieeexplore.ieee.org/document/9852462/ |
| work_keys_str_mv | AT maimohamed futureactivitiespredictionframeworkinsmarthomesenvironment AT aymanelkilany futureactivitiespredictionframeworkinsmarthomesenvironment AT neamateltazi futureactivitiespredictionframeworkinsmarthomesenvironment |