Al Farid Fahmid, Bari Ahsanul, Miah Abu Saleh Musa, Mansor Sarina, Uddin Jia, Kumaresan S Prabha
Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia.
Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology (BAUST), Saidpur 5311, Bangladesh.
J Imaging. 2025 Jun 3;11(6):182. doi: 10.3390/jimaging11060182.
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.
环境辅助生活（AAL）利用技术来支持老年人和残疾人。这些系统中的一个关键挑战是高效的人类活动识别（HAR）。然而，尚无研究系统地比较单视图（SV）和多视图（MV）人类活动识别方法。本综述通过分析从单视图到多视图识别系统的演变，涵盖基准数据集、特征提取方法和分类技术，来弥补这一差距。我们研究了活动识别系统如何使用针对环境辅助生活进行优化的先进深度学习模型过渡到多视图架构，从而提高准确性和鲁棒性。此外，我们探索了广泛的机器学习和深度学习模型，包括卷积神经网络（CNN）、循环神经网络（RNN）、长短期记忆（LSTM）网络、时间卷积网络（TCN）和图卷积网络（GCN），以及适用于计算资源有限环境的轻量级迁移学习方法。讨论了数据修复、隐私和泛化等关键挑战，以及传感器融合和先进学习策略等潜在解决方案。本研究提供了对近期进展和未来方向的全面见解，指导了用于环境辅助生活应用的智能、高效且符合隐私要求的人类活动识别系统的开发。
