Smartphone as unobtrusive sensor for real-time sleep recognition

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 im...

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Main Authors: Montanini, Laura, Sabino, Nicola, Spinsante, Susanna, Gambi, Ennio
Format: Conference Proceeding
Language:English
Subjects:
activity recognition
Batteries
Error correction
Mobile application
Monitoring
Radio frequency
Real-time systems
sleep monitoring
smartphone sensing
Support vector machines
Training
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creator Montanini, Laura
Sabino, Nicola
Spinsante, Susanna
Gambi, Ennio
description 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.
doi_str_mv 10.1109/ICCE.2018.8326220
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source IEEE Xplore All Conference Series
subjects activity recognition
Batteries
Error correction
Mobile application
Monitoring
Radio frequency
Real-time systems
sleep monitoring
smartphone sensing
Support vector machines
Training
title Smartphone as unobtrusive sensor for real-time sleep recognition
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