Citation

Bari, Ahsanul and Abdul Karim, Hezerul and Farid, Fahmid Al and Asaduzzaman, Mina and Amirabdollahian, Farshid and Mansor, Sarina (2024) Multi-View Human Activity Recognition in Ambient Assisted Living Using Lightweight Deep Learning Models. In: 5th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2024, 30 - 31 October 2024, Kuala Lumpur, Malaysia.

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Abstract

Human Activity Recognition (HAR) is crucial for the development of intelligent assistive technologies in Ambient Assisted Living (AAL) environments. This paper proposes an innovative method for Multi-View Human Activity Recognition (MV-HAR) using lightweight deep learning models, specifically MobileNet and Cyclone-CNN (CCNet), to achieve quick and precise activity detection. Utilizing the Robot House MultiView Human Activity Recognition (RHM-HAR) dataset, which contains four different views—front, back, ceiling (omni), and mobile robot—our models effectively address challenges related to viewpoint variation and motion dynamics. The dataset includes 14 multi-view daily living action classes, providing a balanced set of synchronized human actions suitable for multi-domain neural network learning. MobileNet and CCNet are employed for their high recognition accuracy, computational efficiency, and real-time application capabilities in AAL scenarios. We propose a Mutual Information (MI)-based method to assess the redundancy and relevance of each viewpoint, ensuring the fusion of multi-view data with minimum redundancy and maximum relevance. Benchmarking results demonstrate that multi-view combinations significantly enhance recognition performance compared to single-view models, particularly in complex activities involving high levels of movement.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Human activity recognition (HAR), lightweight deep learning,
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	20 Feb 2025 07:34
Last Modified:	20 Feb 2025 08:17
URII:	http://shdl.mmu.edu.my/id/eprint/13527

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