Qi Wen, Xu Xiangmin, Qian Kun, Schuller Bjorn W, Fortino Giancarlo, Aliverti Andrea
IEEE J Biomed Health Inform. 2025 Apr;29(4):2425-2438. doi: 10.1109/JBHI.2024.3406737. Epub 2025 Apr 4.
This scoping review paper redefines the Artificial Intelligence-based Internet of Things (AIoT) driven Human Activity Recognition (HAR) field by systematically extrapolating from various application domains to deduce potential techniques and algorithms. We distill a general model with adaptive learning and optimization mechanisms by conducting a detailed analysis of human activity types and utilizing contact or non-contact devices. It presents various system integration mathematical paradigms driven by multimodal data fusion, covering predictions of complex behaviors and redefining valuable methods, devices, and systems for HAR. Additionally, this paper establishes benchmarks for behavior recognition across different application requirements, from simple localized actions to group activities. It summarizes open research directions, including data diversity and volume, computational limitations, interoperability, real-time recognition, data security, and privacy concerns. Finally, we aim to serve as a comprehensive and foundational resource for researchers delving into the complex and burgeoning realm of AIoT-enhanced HAR, providing insights and guidance for future innovations and developments.
这篇范围综述论文通过系统地从各个应用领域进行推断，以推导潜在的技术和算法，从而重新定义了基于人工智能的物联网（AIoT）驱动的人类活动识别（HAR）领域。我们通过对人类活动类型进行详细分析，并利用接触式或非接触式设备，提炼出了一个具有自适应学习和优化机制的通用模型。它呈现了由多模态数据融合驱动的各种系统集成数学范式，涵盖了复杂行为的预测，并重新定义了用于HAR的有价值的方法、设备和系统。此外，本文还针对从简单的局部动作到群体活动等不同应用需求，建立了行为识别的基准。它总结了开放的研究方向，包括数据多样性和数量、计算限制、互操作性、实时识别、数据安全和隐私问题。最后，我们旨在为深入研究AIoT增强的HAR这一复杂且新兴领域的研究人员提供全面的基础资源，为未来的创新和发展提供见解和指导。
