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https://doi.org/10.24138/jcomss.v14i3.584

Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone

Ennio Gambi ; Dipartimento di Ingegneria dell’Informazione, Universita Politecnica delle Marche, Ancona, Italy
Simone Barbetta ; Dipartimento di Ingegneria dell’Informazione, Universita Politecnica delle Marche, Ancona, Italy
Adelmo De Santis ; Dipartimento di Ingegneria dell’Informazione, Universita Politecnica delle Marche, Ancona, Italy
Manola Ricciuti ; Dipartimento di Ingegneria dell’Informazione, Universita Politecnica delle Marche, Ancona, Italy

Puni tekst: engleski, pdf (2 MB) str. 211-217 preuzimanja: 111* citiraj
APA 6th Edition
Gambi, E., Barbetta, S., De Santis, A. i Ricciuti, M. (2018). Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone. Journal of Communications Software and Systems, 14 (3), 211-217. https://doi.org/10.24138/jcomss.v14i3.584
MLA 8th Edition
Gambi, Ennio, et al. "Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone." Journal of Communications Software and Systems, vol. 14, br. 3, 2018, str. 211-217. https://doi.org/10.24138/jcomss.v14i3.584. Citirano 18.11.2019.
Chicago 17th Edition
Gambi, Ennio, Simone Barbetta, Adelmo De Santis i Manola Ricciuti. "Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone." Journal of Communications Software and Systems 14, br. 3 (2018): 211-217. https://doi.org/10.24138/jcomss.v14i3.584
Harvard
Gambi, E., et al. (2018). 'Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone', Journal of Communications Software and Systems, 14(3), str. 211-217. https://doi.org/10.24138/jcomss.v14i3.584
Vancouver
Gambi E, Barbetta S, De Santis A, Ricciuti M. Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone. Journal of Communications Software and Systems [Internet]. 2018 [pristupljeno 18.11.2019.];14(3):211-217. https://doi.org/10.24138/jcomss.v14i3.584
IEEE
E. Gambi, S. Barbetta, A. De Santis i M. Ricciuti, "Self-Adaptive and Lightweight Real-Time Sleep Recognition With Smartphone", Journal of Communications Software and Systems, vol.14, br. 3, str. 211-217, 2018. [Online]. https://doi.org/10.24138/jcomss.v14i3.584

Sažetak
It is widely recognized that sleep is a basic phys- iological process having fundamental effects on human health, performance and well-being. Such evidence stimulates the re- search of solutions to foster self-awareness of personal sleeping habits, and correct living environment management policies to encourage sleep. In this context, the use of mobile technologies powered with automatic sleep recognition capabilities can be helpful, and ubiquitous computing devices like smartphones can be leveraged as proxies to unobtrusively analyse the human behaviour. To this aim, we propose a real-time sleep recognition methodology relied on a smartphone equipped with a mobile app that exploits contextual and usage information to infer sleep habits. During an initial training stage, the selected features are processed by k-Nearest Neighbors, Decision Tree, Random Forest, and Support Vector Machine classifiers, to select the best performing one. Moreover, a 1st-order Markov Chain is applied to improve the recognition performance. Experimental results, both offline in a Matlab environment, and online through a fully functional Android app, demonstrate the effectiveness of the proposed approach, achieving acceptable results in term of Precision, Recall, and F1-score.

Ključne riječi
Mobile application; smartphone sensing; sleep monitoring; activity recognition

Hrčak ID: 205745

URI
https://hrcak.srce.hr/205745

Posjeta: 167 *