Demrozi Florenc, Pravadelli Graziano, Bihorac Azra, Rashidi Parisa
Department of Computer Science, University of Verona, Italy.
Division of Nephrology, Hypertension, & Renal Transplantation, College of Medicine, University of Florida, Gainesville, FL, USA.
IEEE Access. 2020;8:210816-210836. doi: 10.1109/access.2020.3037715. Epub 2020 Nov 16.
In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.
在过去十年中，人类活动识别（HAR）已成为一个充满活力的研究领域，尤其是因为我们日常生活中存在的智能手机、智能手表和摄像机等电子设备的普及。此外，深度学习和其他机器学习算法的进步使研究人员能够在包括体育、健康和福祉应用在内的各个领域使用HAR。例如，HAR被认为是最有前途的辅助技术工具之一，通过日常活动监测老年人的认知和身体功能来支持他们的日常生活。本综述重点关注机器学习在结合生理和环境传感器开发基于惯性传感器的HAR应用中的关键作用。
