Smart Wearable Technologies for Sleep and Health Monitoring

Dear Colleagues,

Sleep disorders and their accompanying health problems are among the most important health issues in the world, and there is a need to develop improved technologies to objectively evaluate sleep, manage sleep, and monitor health status during sleep. Irregular sleep patterns have been linked to a higher risk of neurodegenerative disorders, cardiovascular disease, and cancer. Physiological dynamic profiles during sleep are strong predictors of many health-related issues, such as cardiovascular events, independent of their daytime profiles. Moreover, many health-related events may occur during sleep while we are unconscious, leading to late diagnosis and treatment.

While there exists medicine, supplements, and meditation therapies to improve sleep disorders, there has been little technological effort to improve and manage sleep and monitor sleep physiology and health. The operation of most current physiological monitors relies on active interaction with a user/expert, the continuous usage of such monitors can be disturbing and disruptive to sleep, and the analysis of the recorded data requires expert interpretation. There is a need for portable, unobtrusive, and inexpensive technologies that can continuously monitor and enhance sleep in real-world environments such as the home.

In this Special Issue, articles regarding the use of technologies and algorithms for sleep and health monitoring and management are invited. The main three themes are:

• Wearable and contactless technologies for continuous in-sleep physiological measurement.
• Automatic algorithms to aid in the analysis and interpretation of physiological data, overcome the intrinsic limitations of human perception and bias, estimate important physiological parameters, and detect and predict health-related issues.
• Smart and unobtrusive intervention technologies for sleep enhancement and management.

Prof. Mohamad Forouzanfar
Guest Editor

Manuscript Submission Information

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• wearables
• sleep
• physiological monitoring
• health monitoring
• artificial intelligence
• machine learning
• deep learning
• biomedical signal processing
• bioinstrumentation
• medical devices