Citation

Bari, Ahsanul and Al Farid, Fahmid and Sarker, Md. Tanjil and Mansor, Sarina and Abdul Karim, Hezerul and Bhuiyan, Md Roman and Bannah, Hasanul (2024) Advancements in Multi-View Human Activity Recognition for Ambient Assisted Living. In: 2024 Multimedia University Engineering Conference (MECON), 23-25 July 2024, Cyberjaya, Malaysia.

Text Advancements in Multi-View Human Activity Recognition for Ambient Assisted Living.pdf - Published Version Restricted to Repository staff only Download (269kB)

Abstract

This Ambient Assisted Living (AAL) uses technology to improve the well-being, autonomy, and security of seniors and disabled individuals. AAL services depend on Human Activity Recognition to detect human behaviors from sensor data. Compared to single-view models, multi-view HAR, which aggregates data from multiple sensors, provides improved accuracy and reliable detection. In this review, we assess recent progress in multi-view HAR for AAL, focusing on the value of lightweight deep learning models. We cover the development of HAR systems, as well as the emergence of multi-view datasets and highly efficient deep learning models that are explicitly designed for AAL scenarios. Furthermore, we address problems such as data synchronization, data protection, and adaptability, as well as possible remedies like sensor fusion and transfer learning. As a result, we also demonstrate the potential of multi-view HAR to increase AAL services and outlining future research trends in providing adaptive, efficient, and privacy-aware systems.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Ambient Assisted Living, lightweight deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Feb 2025 03:57
Last Modified:	06 Feb 2025 03:57
URII:	http://shdl.mmu.edu.my/id/eprint/13359

Downloads

Edit (login required)