Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect

• Published in: Expert systems with applications Vol. 202; p. 117362
• Main Authors: Virginia Anikwe, Chioma, Friday Nweke, Henry, Chukwu Ikegwu, Anayo, Adolphus Egwuonwu, Chukwunonso, Uchenna Onu, Fergus, Rita Alo, Uzoma, Wah Teh, Ying
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.09.2022; Elsevier BV
• Subjects: Algorithms; Coronavirus; Data analysis; Data collection; Data transmission; Decision trees; Deep learning; Diagnosis; Disease; Disease management; Evaluation; Feature extraction; Health monitoring; Internet of things; Machine learning; Medical diagnosis; Mobile and wearable sensors; Monitoring systems; Parkinson's disease; Remote monitoring; Remote sensors; Review; Sensors; Support vector machines; System effectiveness; Telemedicine; Wearable computers; Wearable technology; Health monitoring; Internet of things; Coronavirus; Machine learning; Disease management; Review; Mobile and wearable sensors
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.117362

•Comprehensive review of health monitoring based sensors.•Categorize the sensors into homogeneous, dual and heterogeneous.•Discuss their strengths, drawbacks and application domains.•Outline major issues and significant solutions.•Highlights future prospect of health monitoring sensors. Mobile and wearable devices embedded with multiple sensors for health monitoring and disease diagnosis are growing fields with the potential to provide efficient means for remote health management. A sensor-based health monitoring system offers an essential mechanism for real-time diagnosis and management to detect/predict, recommend treatment and prevent the onset of diseases. This paper aims to synthesize the research efforts on mobile and wearable sensors for health monitoring. It will investigate sensors, components of health monitoring systems, major application areas, challenges, and solutions faced during the implementation of health monitoring systems by researchers and practitioners. It was observed that sensors embedded in mobile and wearable devices for health monitoring are broadly categorized into homogeneous, dual, and heterogeneous sensors. In health monitoring, heterogeneous sensor-based is widely implemented and the most effective due to its ability to combine multiple sensors from various domains. The fusion of multiple sensors provides reliability, credibility, and better accuracy for monitoring multiple health parameters. We observed that researchers follow established procedures such as data collection, data transmission, preprocessing, feature extraction and development, data analysis, and evaluation of different algorithms for implementation of the health monitoring system. Supervised machine learning algorithms such as support vector machine, decision tree, k-nearest neighbors, and deep learning methods were the most implemented methods, while accuracy was the favored evaluation measure for health monitoring. Generally, we found that a health monitoring system is implemented to resolve health issues in the areas of human activity and posture monitoring, sleep disorder, sleep stage detection, fall monitoring in the elderly, depression, and mood swing detection. Other important areas include Parkinson’s disease management, cardiac diseases monitoring, disease diagnosis, and well-being, and Corona virus detection and contact tracing to minimize infection rate. Furthermore, the review succinctly highlights various challenges impeding the development of sensor-based health monitoring systems with significant solutions that were recommended in the literature to ameliorate these challenges discussed. From the review, it can be acknowledged that various research efforts have been conducted to develop effective health monitoring systems, and many new systems have been implemented. However, there is still much work to be done which we have also discussed under future prospects.