Citation

Lim, Josiah Wey Tsen and Tee, Connie and Goh, Michael Kah Ong (2025) Exploring Activities of Daily Living Among the Elderly through Machine Learning Techniques. International Journal on Robotics, Automation and Sciences, 7 (1). pp. 35-46. ISSN 2682-860X

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Abstract

Activities of daily living (ADLs) is a term that is used to describe the activities performed in everyday life that involves the motion of the human body such as eating, walking,and sitting. ADLs can be used to determine the state of elderly people as a decline in ADL performance will generally mean a decline in the human body. It can act as an early indicator if an elderly person is experiencing underlying illness or health issue. This project aims to detect five different ADLs which are eating, cooking, sweeping, walking, and sitting and standing. A dataset was collected from twenty individuals performing each ADL at two different angles, a front view and a side view. A computervision-based human pose estimation technique is used to extract the human body keypoints. These keypoint values are then processed and fit into multiple deep learning models for analysis. In this study, five different deep learning models namely LSTM, Bi-LSTM, CNN, RNN and Transformer models have been evaluated. The performance of each model is analysedand discussed. It was determined that the CNN model performed the best achieving a categorical accuracy of 82.86%

Item Type:	Article
Uncontrolled Keywords:	Machine learning, motion, computer vision
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	26 Jun 2025 01:16
Last Modified:	26 Jun 2025 01:16
URII:	http://shdl.mmu.edu.my/id/eprint/14064

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