Abo-Zahhad, M., Elsayed, M., Sayed, M., Abdel Malek, A., Fawaz, A., Sharshar, A., Abo Zahhad, M. (2023). Design of Smart Wearable System for Sleep Tracking Using SVM and Multi-Sensor Approach. JES. Journal of Engineering Sciences, 51(4), 1-15. doi: 10.21608/jesaun.2023.205964.1220
Mohammed M. Abo-Zahhad; Moaaz Elsayed; Mohammed Sayed; Ahmed Abdel Malek; AbdelRahman Fawaz; Ahmed Sharshar; Mohamed Abo Zahhad. "Design of Smart Wearable System for Sleep Tracking Using SVM and Multi-Sensor Approach". JES. Journal of Engineering Sciences, 51, 4, 2023, 1-15. doi: 10.21608/jesaun.2023.205964.1220
Abo-Zahhad, M., Elsayed, M., Sayed, M., Abdel Malek, A., Fawaz, A., Sharshar, A., Abo Zahhad, M. (2023). 'Design of Smart Wearable System for Sleep Tracking Using SVM and Multi-Sensor Approach', JES. Journal of Engineering Sciences, 51(4), pp. 1-15. doi: 10.21608/jesaun.2023.205964.1220
Abo-Zahhad, M., Elsayed, M., Sayed, M., Abdel Malek, A., Fawaz, A., Sharshar, A., Abo Zahhad, M. Design of Smart Wearable System for Sleep Tracking Using SVM and Multi-Sensor Approach. JES. Journal of Engineering Sciences, 2023; 51(4): 1-15. doi: 10.21608/jesaun.2023.205964.1220
Design of Smart Wearable System for Sleep Tracking Using SVM and Multi-Sensor Approach
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.
[1] D. Pacheco, “How is actigraphy used to evaluate sleep?” Sleep Foundation, 01-Oct-2021. [Online]. Available: https://www.sleepfoundation.org/sleep-studies/actigraphy. [Accessed: 25-March-2023].
[2] K. Saleem, I. S. Bajwa, N. Sarwar, W. Anwar, and A. Ashraf, “IoT healthcare: Design of smart and cost-effective sleep quality monitoring system,” Journal of Sensors, vol. 2020, pp. 1–17, 2020.
[3] Kushida CA, Littner MR, Morgenthaler T, Alessi CA, Bailey D, Coleman J, Friedman L, Hirshkowitz M, Kapen S, Kramer M, Lee-Chiong T, Loube DL, Owens J, Pancer JP, Wise M. Practice parameters for the indications for polysomnography and related procedures: an update for 2005. Sleep. 2005 Apr;28(4):499-521.
[5] A. Crivello, P. Barsocchi, M. Girolami and F. Palumbo, "The Meaning of Sleep Quality: A Survey of Available Technologies," in IEEE Access, vol. 7, pp. 167374-167390, 2019, DOI: 10.1109/ACCESS.2019.2953835.
[6] C. Lashkari, “How do wearables track sleep?,” News Medical, 27-Feb-2019. [Online]. Available: https://www.news-medical.net/health/How-Do-Wearables-Track-Sleep.aspx. [Accessed: 26-25-March-2023].
[7] J. L. Martin and A. D. Hakim, “Wrist actigraphy,” Chest, vol. 139, no. 6, pp. 1514–1527, 2011.
[8] A. Alakuijala, T. Sarkanen, T. Jokela, and M. Partinen, “Accuracy of actigraphy compared to concomitant ambulatory polysomnography in narcolepsy and other sleep disorders,” Frontiers in Neurology, vol. 12, 2021.
[9] A. Hunt and C. McGinley, “What is a Fitbit and how does it work? Plus, everything you need to know about setting up your Fitbit,” Womanandhome.com, 23-Jun-2021. [Online]. Available: https://www.womanandhome.com/health-and-wellbeing/how-to-set-up-a-fitbit-a-guide-206915/. [Accessed: 08-25-March-2023].
[10] S. Burkart, M. W. Beets, B. Armstrong, E. T. Hunt, R. Dugger, L. von Klinggraeff, A. Jones, D. E. Brown, and R. G. Weaver, “Comparison of multichannel and single-channel wrist-based devices with polysomnography to measure sleep in children and adolescents,” Journal of Clinical Sleep Medicine, vol. 17, no. 4, pp. 645–652, 2021.
[11] M. Altini and H. Kinnunen, “The promise of sleep: A multi-sensor approach for accurate sleep stage detection using the Oura Ring,” Sensors, vol. 21, no. 13, p. 4302, 2021.
[12] C. Crider, “Oura Ring: A comprehensive review,” Healthline, 25-Feb-2022. [Online]. Available: https://www.healthline.com/health/ fitness/oura-ring. [Accessed: 27-25-March-2023].
[13] M. Asgari Mehrabadi, I. Azimi, F. Sarhaddi, A. Axelin, H. Niela-Vilén, S. Myllyntausta, S. Stenholm, N. Dutt, P. Liljeberg, and A. M. Rahmani, “Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: An instrument validation study,” JMIR mHealth and uHealth, vol. 8, no. 10, 2020.
[14] D. Pacheco, “Improving sleep quality: How is it calculated?,” Sleep Foundation, 24-Jun-2021. [Online]. Available: https://www.sleepfoundation.org/sleep-hygiene/how-is-sleep-quality-calculated. [Accessed: 25-March-2023].
[15] Almazaydeh L, Elleithy K, Faezipour M. Obstructive sleep apnea detection using SVM-based classification of ECG signal features. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4938-41. doi: 10.1109/EMBC.2012.6347100. PMID: 23367035.
[16] Fell, Jürgen, J. Röschke, and C. Schäffner "Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures." Electroencephalography and Clinical Neurophysiology 98.5 (1996): 401-410.
[17] M. El-Diasty, “An accurate heading solution using MEMS-based gyroscope and Magnetometer Integrated System (preliminary results),” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. II-2, pp. 75–78, 2014.
[18] M. Abo-Zahhad, M. S. Sayed and A. H. Ahmed H. Abd El-Malek, "An IoT-based Smart Wearable System for Remote Health Monitoring," 2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC), 2021, pp. 192-197, DOI: 10.1109/JAC-ECC54461.2021.9691431.
[20] Suni, E. and Vyas, N., 2021. Stages of Sleep - Sleep Foundation. [online] Sleepfoundation.org. Available at: <https://www.sleepfoundation.org/how-sleep-works/stages-of-sleep> [Accessed 25-March-2023].
[21] I. Arun Faisal, T. Waluyo Purboyo, and A. Siswo Raharjo Ansori, “A review of accelerometer sensor and gyroscope sensor in IMU sensors on motion capture,” Journal of Engineering and Applied Sciences, vol. 15, no. 3, pp. 826–829, 2019.
[22] M. El-Diasty, “An accurate heading solution using MEMS-based gyroscope and Magnetometer Integrated System (preliminary results),” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. II-2, pp. 75–78, 2014.
[23] Conference on Communications, Signal Processing, and their Applications (ICCSPA), 2021. Available: 10.1109/iccspa49915.2021.9385761 [Accessed 25-March-2023].
[24] A. Siddiqui, “Huami Amazfit GTS review: Surprisingly different from the Apple Watch,” xda, 14-Dec-2019. [Online]. Available: https://www.xda-developers.com/huami-amazfit-gts-review-surprisingly-different-apple-watch-fitness-tracker-smartwatch/. [Accessed: 25-March-2023].
[25] J. Cheung, J. M. Zeitzer, H. Lu, and E. Mignot, “Validation of minute-to-minute scoring for sleep and wake periods in a consumer wearable device compared to an actigraphy device,” Sleep Science and Practice, vol. 2, no. 1, 2018.
[26] T. Evgeniou and M. Pontil, “Support Vector Machines: Theory and applications,” Machine Learning and Its Applications, pp. 249–257, 2001.
[27] Alomar K, Aysel HI, Cai X. Data Augmentation in Classification and Segmentation: A Survey and New Strategies. Journal of Imaging. 2023 Feb 17;9(2):46.