A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living

Citation

Al Farid, Fahmid and Bari, Ahsanul and Miah, Abu Saleh Musa and Mansor, Sarina and Uddin, Jia and Kumaresan, S. Prabha (2025) A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living. Journal of Imaging, 11 (6). p. 182. ISSN 2313-433X

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Abstract

Ambient Assisted Living (AAL) leverages technology to support the elderly and individuals with disabilities. A key challenge in these systems is efficient Human Activity Recognition (HAR). However, no study has systematically compared single-view (SV) and multi-view (MV) Human Activity Recognition approaches. This review addresses this gap by analyzing the evolution from single-view to multi-view recognition systems, covering benchmark datasets, feature extraction methods, and classification techniques. We examine how activity recognition systems have transitioned to multi-view architectures using advanced deep learning models optimized for Ambient Assisted Living, thereby improving accuracy and robustness. Furthermore, we explore a wide range of machine learning and deep learning models—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and Graph Convolutional Networks (GCNs)—along with lightweight transfer learning methods suitable for environments with limited computational resources. Key challenges such as data remediation, privacy, and generalization are discussed, alongside potential solutions such as sensor fusion and advanced learning strategies. This study offers comprehensive insights into recent advancements and future directions, guiding the development of intelligent, efficient, and privacy-compliant Human Activity Recognition systems for Ambient Assisted Living applications.

Item Type: Article
Uncontrolled Keywords: Ambient Assisted Living, lightweight deep learning, activity recognition, machine learning, wearable sensors, smartphones, context-aware, deep learning
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 28 Jul 2025 07:40
Last Modified: 30 Jul 2025 19:53
URII: http://shdl.mmu.edu.my/id/eprint/14303

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