Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review

• Published in: Journal of biomedical informatics Vol. 87; pp. 138 - 153
• Main Authors: Qi, Jun, Yang, Po, Waraich, Atif, Deng, Zhikun, Zhao, Youbing, Yang, Yun
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2018
• Subjects: Internet of Things; Physical activity monitoring; Physical activity recognition; Sensor-based; Systematic review; Systematic review; Internet of Things; Physical activity recognition; Sensor-based; Physical activity monitoring
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2018.09.002

•It summarises the state-of-the-art in traditional PARM methodologies for healthcare.•It identifies new research trends and challenges of PARM studies in IoT environments.•It considers successful case studies in the area and look at the possible future industrial applications in smart healthcare. Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare.