Design of Smart Wearable System for Sleep Tracking Using SVM and Multi-Sensor Approach | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 4, Volume 51, Issue 4, July and August 2023, Page 1-15 PDF (588.27 K) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2023.205964.1220 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mohammed M. Abo-Zahhad   1; Moaaz Elsayed2; Mohammed Sayed2; Ahmed Abdel Malek3; AbdelRahman Fawaz2; Ahmed Sharshar2; Mohamed Abo Zahhad2
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| 1Electrical Engineering, Faculty of Engineering, Sohag University, Sohag, Egypt | ||||
| 2Department of Electronics and Communications Engineering, Egypt-Japan University of Science and Technology, New Borg El-Arab City, Alexandria, Egypt. | ||||
| 3Department of Electronics and Communications Engineering, Egypt-Japan University of Science and Technology, New Borg El-Arab City, Alexandria, Egypt | ||||
| Abstract | ||||
| Healthcare has been considered one of the main issues to be spotted and improved in a high manner. Thus, many technology trends are customized to be used in the development of the field of healthcare. One of the fields that highly affects health is sleeping, therefore, the importance of developing a portable and cost-affordable sleep-tracking system has arisen. Getting enough good-quality sleep is essential for living a healthy life. This could be done by monitoring vital signals that affect sleep quality such as heart rate, blood oxygen saturation, and positioning. Furthermore, these parameters could be used to detect sleep stages. Detecting sleep stages provides the ability to specify sleep quality and how to get better sleep hygiene. In this paper, a sleep quality monitoring system using commercial off-the-shelf sensors has been developed. The main aims are to make the system cheap, besides being portable, lightweight, and easy to use with better sleep quality and sleep stages accuracies compared to recently published systems. Based on the personalized data collected, the system could identify the sleep onset latency, the wake after sleep onset, the total sleep time, and the pattern based on the step before. Then, users would know about their quality of sleep and sleeping habits, which will be directly reflected in their health and well-being. The obtained results indicate that sleep quality accuracy is 97.5% and sleep stages accuracy is 67.5% which are better than similar systems used with commercial off-the-shelf sensors. | ||||
| Keywords | ||||
| Internet of Things; Wearable electronics; Sleep tracking | ||||
| References | ||||
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