Wearables-assisted smart health monitoring for sleep quality prediction using optimal deep learning
Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed for fitness and wellness as they enable people to observe their day-to-day health status. These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph devices come under the...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Multidisciplinary Digital Publishing Institute
2023
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/112329/2/112329_Wearables-assisted%20smart%20health%20monitoring_SCOPUS.pdf http://irep.iium.edu.my/112329/3/112329_Wearables-assisted%20smart%20health%20monitoring.pdf http://irep.iium.edu.my/112329/ https://www.mdpi.com/2071-1050/15/2/1084/pdf?version=1673000693 http://doi.org/10.3390/su15021084 |
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| Summary: | Wearable devices such as smartwatches, wristbands, and GPS shoes are commonly employed
for fitness and wellness as they enable people to observe their day-to-day health status.
These gadgets encompass sensors to accumulate data related to user activities. Clinical act graph
devices come under the class of wearables worn on the wrist to compute the sleep parameters by
storing sleep movements. Sleep is very important for a healthy lifestyle. Inadequate sleep can
obstruct physical, emotional, and mental health, and could result in several illnesses such as insulin
resistance, high blood pressure, heart disease, stress, etc. Recently, deep learning (DL) models have
been employed for predicting sleep quality depending upon the wearables data from the period of
being awake. In this aspect, this study develops a new wearables-assisted smart health monitoring
for sleep quality prediction using optimal deep learning (WSHMSQP-ODL) model. The presented
WSHMSQP-ODL technique initially enables the wearables to gather sleep-activity-related data. Next,
data pre-processing is performed to transform the data into a uniform format. For sleep quality
prediction, the WSHMSQP-ODL model uses the deep belief network (DBN) model. To enhance
the sleep quality prediction performance of the DBN model, the enhanced seagull optimization
(ESGO) algorithm is used for hyperparameter tuning. The experimental results of the WSHMSQPODL
method are examined under different measures. An extensive comparison study shows the
significant performance of the WSHMSQP-ODL model over other models. |
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