In Proceedings of the IEEE International Conference on Healthcare Informatics. Towards benchmarked sleep detection with wrist-worn sensing units. Borazio Marko, Berlin Eugen, Kücükyildiz Nagihan, Scholl Philipp, and Laerhoven Kristof Van.A comparative review on sleep stage classification methods in patients and healthy individuals. Boostani Reza, Karimzadeh Foroozan, and Nami Mohammad.Automating sleep stage classification using wireless, wearable sensors. McGee, O’Brien Megan K., Shawen Nicholas, Rogers John A., Lieber Richard L., Reid Kathryn J., Zee Phyllis C., and Jayaraman Arun. RestEaZe: Low-power accurate sleep monitoring using a wearable multi-sensor ankle band. Bobovych Stanislav, Sayeed Fahad, Banerjee Nilanjan, Robucci Ryan, and Allen Richard P.Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals. Beattie Zachary, Oyang Y., Statan A., Ghoreyshi A., Pantelopoulos A., Russell A., and Heneghan C.In Proceedings of the 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom’16). An unobtrusive sleep monitoring system for the human sleep behaviour understanding. Barsocchi Paolo, Bianchini Monica, Crivello Antonino, Rosa Davide La, Palumbo Filippo, and Scarselli Franco.Oral appliance in sleep apnea treatment: Respiratory and clinical effects and long-term adherence. Bachour Patrick, Bachour Adel, Kauppi Paula, Maasilta Paula, Mäkitie Antti, and Palotie Tuula.Remote monitoring of human vital signs using mm-wave FMCW radar. Alizadeh Mostafa, Shaker George, Almeida João Carlos Martins De, Morita Plinio Pelegrini, and Safavi-Naeini Safeddin.Review on biomedical sensors, technologies and algorithms for diagnosis of sleep disordered breathing: Comprehensive survey. Ahmadzadeh Sahar, Luo Jikui, and Wiffen Richard.CUIDATS: An RFID–WSN hybrid monitoring system for smart healthcare environments. Adame Toni, Bel Albert, Carreras Anna, Melia-Segui Joan, Oliver Miquel, and Pous Rafael.Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation. Aboalayon Khald Ali I., Faezipour Miad, Almuhammadi Wafaa S., and Moslehpour Saeid.We finally discuss several open issues and future research directions in the area of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring.

We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring.

Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Quality sleep is very important for a healthy life.

Sleep monitor review