A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living
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 a...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/6/182 |
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| author | Fahmid Al Farid Ahsanul Bari Abu Saleh Musa Miah Sarina Mansor Jia Uddin S. Prabha Kumaresan |
| author_facet | Fahmid Al Farid Ahsanul Bari Abu Saleh Musa Miah Sarina Mansor Jia Uddin S. Prabha Kumaresan |
| author_sort | Fahmid Al Farid |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e9da8e2e2a274a68bc335f25cd47ede2 |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-e9da8e2e2a274a68bc335f25cd47ede22025-08-20T02:20:58ZengMDPI AGJournal of Imaging2313-433X2025-06-0111618210.3390/jimaging11060182A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted LivingFahmid Al Farid0Ahsanul Bari1Abu Saleh Musa Miah2Sarina Mansor3Jia Uddin4S. Prabha Kumaresan5Faculty of Engineering, Multimedia University, Cyberjaya 63100, MalaysiaFaculty of Engineering, Multimedia University, Cyberjaya 63100, MalaysiaDepartment of Computer Science and Engineering, Bangladesh Army University of Science and Technology (BAUST), Saidpur 5311, BangladeshFaculty of Engineering, Multimedia University, Cyberjaya 63100, MalaysiaAI and Big Data Department, Woosong University, Daejeon 34606, Republic of KoreaCentre for Image and Vision Computing (CIVC), COE for Artificial Intelligence, Faculty of Artificial Intelligence and Engineering (FAIE), Multimedia University, Cyberjaya 63100, MalaysiaAmbient 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.https://www.mdpi.com/2313-433X/11/6/182Ambient Assisted Livinglightweight deep learningactivity recognitionmachine learningwearable sensorssmartphones |
| spellingShingle | Fahmid Al Farid Ahsanul Bari Abu Saleh Musa Miah Sarina Mansor Jia Uddin S. Prabha Kumaresan A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living Journal of Imaging Ambient Assisted Living lightweight deep learning activity recognition machine learning wearable sensors smartphones |
| title | A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living |
| title_full | A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living |
| title_fullStr | A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living |
| title_full_unstemmed | A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living |
| title_short | A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living |
| title_sort | structured and methodological review on multi view human activity recognition for ambient assisted living |
| topic | Ambient Assisted Living lightweight deep learning activity recognition machine learning wearable sensors smartphones |
| url | https://www.mdpi.com/2313-433X/11/6/182 |
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