The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data

Zamani, Abu Sarwar; Hashim, Aisha Hassan Abdalla; Akhtar, Md. Mobin; Samdani, Faizan; Siddiqui, Ahmad Talha; Alluhayb, Adel; Hamza, Manar Ahmed; Ahmad, Naved

doi:10.1016/j.jksus.2023.102927

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Cited by 3 publications

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Harmonizing Health: Early Detection of Hormonal Imbalances Through Smart Wearables and Ensemble Deep Learning Models

Deepa,

Kavya,

Duraivelu

et al. 2024

Information Systems Engineering and Management

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Harmonizing Health: Early Detection of Hormonal Imbalances Through Smart Wearables and Ensemble Deep Learning Models

Deepa,

Kavya,

Duraivelu

et al. 2024

Information Systems Engineering and Management

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

Duhayyim,

Sharif,

Munjal

et al. 2024

SN COMPUT. SCI.

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MindWatch Leveraging Wearable Technology for Real-Time Monitoring and Support of Student Mental Health

Mehnaz,

Basthikodi,

Rahiman

et al. 2025

Advances in Educational Technologies and Instructional Design

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The increasing demands of academics and societal issues are leading to a rise in health concerns, like anxiety and stress among college students affecting their performance and overall well-being significantly. This chapter delves into the progressions in gadgets such as smartwatches and biosensors that monitor vital signs like heart rate variability as well as stress and sleep patterns instantly. These innovations are boosted by intelligence (AI) allowing for tailored guidance and interventions to support well-being effectively. Using gadgets has proven to be effective, in offering observations into the mental well-being of students by recognizing problems early on and promoting self-care practices in educational environments. However, these devices face challenges related to safeguarding data privacy accurately and ensuring accessibility, which must be addressed for their broader adoption in schools and universities.

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The prediction of sleep quality using wearable-assisted smart health monitoring systems based on statistical data

Cited by 3 publications

References 36 publications

Harmonizing Health: Early Detection of Hormonal Imbalances Through Smart Wearables and Ensemble Deep Learning Models

Harmonizing Health: Early Detection of Hormonal Imbalances Through Smart Wearables and Ensemble Deep Learning Models

Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

MindWatch Leveraging Wearable Technology for Real-Time Monitoring and Support of Student Mental Health

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