With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.
翻译:随着互联网(IoT)和人工智能技术的迅速发展,人类活动识别(HAR)已应用于多个领域,如安全和监视、人类-机器人互动和娱乐等,尽管已经发表了一些调查和审查文件,但缺乏侧重于使用可磨损感应器的保健应用的HAR概览文件,因此,我们通过介绍这份概览文件填补了空白,特别是我们介绍了我们的项目,以说明HAR医疗应用系统的设计;我们的项目包括早期识别特护单位病人的人类活动,以及对Duchenne肌肉萎缩症(DMD)病人的口服分析。我们涵盖HAR系统设计的基本组成部分,包括感应因素(例如类型、数目和安置地点)、AI模型选择(例如经典机器学习模型与深层学习模型)和特征工程。此外,我们强调这种以保健为导向的HAR系统的挑战,并为医疗和计算机科学界提出若干研究机会。