Sleep is fundamental to health, performance and well-being. Studies demonstrate that, in some countries, sleep disorders are reaching epidemic levels. For this reason, automatic sleep recognition systems can be helpful, on the one hand, to foster self awareness of own habits and, on the other, to implement environment management policies to encourage sleep. In this context, we propose an unobtrusive smartphone application which relies on contextual and usage information to infer sleep habits in real-time. We test selected features using kNearest Neighbors, Decision Tree, Random Forest, and Support Vector Machine classifiers. Moreover, we exploit a 1st-order Markov Chain to improve the algorithm's performance. Experimental results demonstrate the effectiveness of the proposed approach, achieving acceptable results in term of Precision, Recall, and F1-score.
Smartphone as unobtrusive sensor for real-time sleep recognition / Montanini, Laura; Sabino, Nicola; Spinsante, Susanna; Gambi, Ennio. - ELETTRONICO. - (2018). (Intervento presentato al convegno 2018 IEEE International Conference on Consumer Electronics (ICCE) tenutosi a Las Vegas, NV, USA, USA nel 12-14/01/2018) [10.1109/ICCE.2018.8326220].
Smartphone as unobtrusive sensor for real-time sleep recognition
Laura Montanini;Susanna Spinsante;Ennio Gambi
2018-01-01
Abstract
Sleep is fundamental to health, performance and well-being. Studies demonstrate that, in some countries, sleep disorders are reaching epidemic levels. For this reason, automatic sleep recognition systems can be helpful, on the one hand, to foster self awareness of own habits and, on the other, to implement environment management policies to encourage sleep. In this context, we propose an unobtrusive smartphone application which relies on contextual and usage information to infer sleep habits in real-time. We test selected features using kNearest Neighbors, Decision Tree, Random Forest, and Support Vector Machine classifiers. Moreover, we exploit a 1st-order Markov Chain to improve the algorithm's performance. Experimental results demonstrate the effectiveness of the proposed approach, achieving acceptable results in term of Precision, Recall, and F1-score.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.