Citation

Hong, Lim Chin and Tee, Connie and Goh, Michael Kah Ong (2022) Activities of Daily Living Recognition Using Deep Learning Approaches. Journal of Logistics, Informatics and Service Science, 9 (4). pp. 129-148. ISSN 2409-2665

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Abstract

Alzheimer’s disease has become a prevalent disease faced by the elderly in Malaysia. Studies believe that early symptoms of the disease can be detected via activities of daily living. Activities of daily living is a term that collectively refer to the basic or fundamental activities performed independently to care for oneself. In this paper, a deep learning approach is presented for activities of daily living recognition to classify daily life activities such as drinking from the cup, eating at a table, reading the book, using the telephone, and walking. A number of long short-term memory (LSTM) variants have been tested in this study. Experiments results demonstrate a promising accuracy of 94% can be achieved using the public Toyota Smarthome dataset.

Item Type:	Article
Uncontrolled Keywords:	Deep Learning, LSTM, Toyota Smarthome Dataset, Classification, Daily life activities
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	16 Mar 2023 01:43
Last Modified:	16 Mar 2023 01:43
URII:	http://shdl.mmu.edu.my/id/eprint/11247

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