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

Ennio Gambi, Simone Barbetta, Adelmo De Santis, Manola Ricciuti


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.


Mobile application; smartphone sensing; sleep monitoring; activity recognition

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