The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data
The technology, which plays a significant role in our lives, has made it possible for many of the appliances and gadgets we use on a daily basis to be monitored and controlled remotely. Health and fitness data is collected by wearable devices attached to patients' bodies. A number of parties co...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Journal of King Saud University: Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018364723003890 |
| _version_ | 1797630593928790016 |
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| author | Abu Sarwar Zamani Aisha Hassan Abdalla Hashim Md. Mobin Akhtar Faizan Samdani Ahmad Talha Siddiqui Adel Alluhayb Manar Ahmed Hamza Naved Ahmad |
| author_facet | Abu Sarwar Zamani Aisha Hassan Abdalla Hashim Md. Mobin Akhtar Faizan Samdani Ahmad Talha Siddiqui Adel Alluhayb Manar Ahmed Hamza Naved Ahmad |
| author_sort | Abu Sarwar Zamani |
| collection | DOAJ |
| description | The technology, which plays a significant role in our lives, has made it possible for many of the appliances and gadgets we use on a daily basis to be monitored and controlled remotely. Health and fitness data is collected by wearable devices attached to patients' bodies. A number of parties could benefit from this technology, including doctors, insurers, and health providers. This technology, including smartwatches, smart ring, smart cloth wristbands, and GPS shoes, is frequently used for fitness and wellness since it allows users to track their day-to-day health. Devices that compute the sleep characteristics by storing sleep movements fall within the category of wearables worn on the wrist. In order to lead a healthy lifestyle, sleep is crucial. Inadequate sleep can harm one's physical, mental, and emotional well-being and increase the risk of developing a number of ailments, including stress, heart disease, high blood pressure, insulin resistance, and other conditions. Deep learning (DL) models have recently been used to forecast sleep-quality based on wearables information from the awake hours. Deep learning has been demonstrated to be capable of predicting sleep efficiency based on wearable data obtained during awake periods. In this regard, this study creates a novel deep learning model for wearables-enabled smart health monitoring system (DLM-WESHMS) for the prediction of sleep quality. The wearables are initially able to collect data linked to sleep-activity using the described DLM-WESHMS approach. The data is then put through pre-processing to create a standard format. Using the DLM-WESHMS, sleep quality is predicted using the deep belief network (DBN) model. The DBN model uses the auto-encoders algorithm (AEA) to predict popularity, which improves the accuracy of its predictions of sleep quality. The experimental outcomes of the DLM-WESHMS approach are investigated using several metrics. The DLM-WESHMS model performs significantly better than other models, according to a thorough comparison analysis. |
| first_indexed | 2024-03-11T11:10:26Z |
| format | Article |
| id | doaj.art-4b147f1e32f44395bc3ee7138bfcecba |
| institution | Directory Open Access Journal |
| issn | 1018-3647 |
| language | English |
| last_indexed | 2024-03-11T11:10:26Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Science |
| spelling | doaj.art-4b147f1e32f44395bc3ee7138bfcecba2023-11-12T04:39:35ZengElsevierJournal of King Saud University: Science1018-36472023-12-01359102927The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical dataAbu Sarwar Zamani0Aisha Hassan Abdalla Hashim1Md. Mobin Akhtar2Faizan Samdani3Ahmad Talha Siddiqui4Adel Alluhayb5Manar Ahmed Hamza6Naved Ahmad7Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia; Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia; Corresponding author.Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, MalaysiaDepartment of Basic Sciences, Riyadh Elm University (REU), Riyadh, Saudi ArabiaDepartment of Computer Engineering, Asia Pacific University, Bukit Jalil, Kuala Lumpur, MalaysiaDepartment of CS & IT, Maulana Azad National Urdu University, IndiaIT & Computer Science Department, College of Science and Humanities, Al Quwaiiyah, Shaqra University, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi ArabiaCollege of Applied Sciences, AlMaarefa University, P.O.Box 71666, Riyadh 13713, Saudi ArabiaThe technology, which plays a significant role in our lives, has made it possible for many of the appliances and gadgets we use on a daily basis to be monitored and controlled remotely. Health and fitness data is collected by wearable devices attached to patients' bodies. A number of parties could benefit from this technology, including doctors, insurers, and health providers. This technology, including smartwatches, smart ring, smart cloth wristbands, and GPS shoes, is frequently used for fitness and wellness since it allows users to track their day-to-day health. Devices that compute the sleep characteristics by storing sleep movements fall within the category of wearables worn on the wrist. In order to lead a healthy lifestyle, sleep is crucial. Inadequate sleep can harm one's physical, mental, and emotional well-being and increase the risk of developing a number of ailments, including stress, heart disease, high blood pressure, insulin resistance, and other conditions. Deep learning (DL) models have recently been used to forecast sleep-quality based on wearables information from the awake hours. Deep learning has been demonstrated to be capable of predicting sleep efficiency based on wearable data obtained during awake periods. In this regard, this study creates a novel deep learning model for wearables-enabled smart health monitoring system (DLM-WESHMS) for the prediction of sleep quality. The wearables are initially able to collect data linked to sleep-activity using the described DLM-WESHMS approach. The data is then put through pre-processing to create a standard format. Using the DLM-WESHMS, sleep quality is predicted using the deep belief network (DBN) model. The DBN model uses the auto-encoders algorithm (AEA) to predict popularity, which improves the accuracy of its predictions of sleep quality. The experimental outcomes of the DLM-WESHMS approach are investigated using several metrics. The DLM-WESHMS model performs significantly better than other models, according to a thorough comparison analysis.http://www.sciencedirect.com/science/article/pii/S1018364723003890HealthcareWearablesSleep-quality predictionDeep learning |
| spellingShingle | Abu Sarwar Zamani Aisha Hassan Abdalla Hashim Md. Mobin Akhtar Faizan Samdani Ahmad Talha Siddiqui Adel Alluhayb Manar Ahmed Hamza Naved Ahmad The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data Journal of King Saud University: Science Healthcare Wearables Sleep-quality prediction Deep learning |
| title | The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data |
| title_full | The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data |
| title_fullStr | The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data |
| title_full_unstemmed | The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data |
| title_short | The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data |
| title_sort | prediction of sleep quality using wearable assisted smart health monitoring systems based on statistical data |
| topic | Healthcare Wearables Sleep-quality prediction Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S1018364723003890 |
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