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Liyakathunisa, , Alsaeedi, A, Jabeen, S and Kolivand, H (2022) Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques. Journal of Ambient Intelligence and Smart Environments, 14 (1). pp. 5-23. ISSN 1876-1364
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Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques.pdf - Accepted Version Download (958kB) | Preview |
Abstract
Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.
| Item Type: | Article |
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| Additional Information: | This is the accepted version of the following paper: Liyakathunisa, et al. ‘Ambient Assisted Living Framework for Elderly Care Using Internet of Medical Things, Smart Sensors, and GRU Deep Learning Techniques’. 1 Jan. 2022 : 5 – 23. Which can be accessed at: https://doi.org/10.3233/ais-210162 |
| Uncontrolled Keywords: | 0801 Artificial Intelligence and Image Processing, 0805 Distributed Computing |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General) |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IOS Press |
| Date Deposited: | 04 Mar 2022 10:55 |
| Last Modified: | 04 Mar 2022 11:00 |
| DOI or ID number: | 10.3233/ais-210162 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/16167 |
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