Département D'informatique et de Mathématique, Université du Québec à Chicoutimi, Chicoutimi, QC G7H 2B1, Canada.
Departement of Information Systems and Quantitative Methods in Management, École de Gestion, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.
Sensors (Basel). 2021 Apr 15;21(8):2786. doi: 10.3390/s21082786.
This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Offline activity recognition is discussed as a starting point to establish the useful concepts of the field, such as sensor types, activity labeling and feature extraction, outlier detection, and machine learning. New challenges and obstacles brought on by real-time centralized activity recognition such as communication, real-time activity labeling, cloud and local approaches, and real-time machine learning in a streaming context are then discussed. Finally, real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise.
这篇综述介绍了医疗保健领域实时分布式活动识别研究挑战的现状和全局概览。离线活动识别被讨论为建立该领域有用概念的起点，例如传感器类型、活动标记和特征提取、异常值检测和机器学习。然后讨论了实时集中式活动识别带来的新挑战和障碍，例如通信、实时活动标记、云和本地方法以及流上下文中的实时机器学习。最后，通过科学文献中的现有实现讨论了实时分布式活动识别，并定义了六个主要的优化角度：处理、内存、通信、能量、时间和准确性。本调查面向任何对分布式人工智能以及活动识别发展感兴趣的读者，无论其专业水平如何。
