Personal informatics systems can help people promote their health and well-being. Recent studies have shown that such systems can be used to infer relevant health indicators such as, e.g., stress, anxiety, and sleeping habits. While automatic detection of sleep has been studied extensively, there is a lack of studies exploring how population and personalized models influence the performance of sleep detection. In this article, we address this challenge by investigating the recognition of sleep/wake stages and high/low sleep quality with a focus on the impact of personalized models. To evaluate our approach, we collect a dataset of physiological signals and self-reports about sleep/wake times and sleep quality score. The dataset contains 6557 hours of sensor data collected using wristbands from 16 participants over one month. Our results show that personalized models perform significantly better than population models for sleep quality recognition, and are comparably good for sleep stage detection. The balanced accuracy for sleep/wake and high/low sleep quality are 92.2% and 61.51%, which are significantly higher than baseline classifiers.

Gashi, S., Alecci, L., Di Lascio, E., Debus, M., Gasparini, F., Santini, S. (2022). The Role of Model Personalization for Sleep Stage and Sleep Quality Recognition Using Wearables. IEEE PERVASIVE COMPUTING, 21(2), 69-77 [10.1109/MPRV.2022.3164334].

The Role of Model Personalization for Sleep Stage and Sleep Quality Recognition Using Wearables

Gasparini, F;
2022

Abstract

Personal informatics systems can help people promote their health and well-being. Recent studies have shown that such systems can be used to infer relevant health indicators such as, e.g., stress, anxiety, and sleeping habits. While automatic detection of sleep has been studied extensively, there is a lack of studies exploring how population and personalized models influence the performance of sleep detection. In this article, we address this challenge by investigating the recognition of sleep/wake stages and high/low sleep quality with a focus on the impact of personalized models. To evaluate our approach, we collect a dataset of physiological signals and self-reports about sleep/wake times and sleep quality score. The dataset contains 6557 hours of sensor data collected using wristbands from 16 participants over one month. Our results show that personalized models perform significantly better than population models for sleep quality recognition, and are comparably good for sleep stage detection. The balanced accuracy for sleep/wake and high/low sleep quality are 92.2% and 61.51%, which are significantly higher than baseline classifiers.
Articolo in rivista - Articolo scientifico
Sleep; Feature extraction; Sensors; Task analysis; Biomedical monitoring; Temperature sensors; Monitoring
English
69
77
9
Gashi, S., Alecci, L., Di Lascio, E., Debus, M., Gasparini, F., Santini, S. (2022). The Role of Model Personalization for Sleep Stage and Sleep Quality Recognition Using Wearables. IEEE PERVASIVE COMPUTING, 21(2), 69-77 [10.1109/MPRV.2022.3164334].
Gashi, S; Alecci, L; Di Lascio, E; Debus, M; Gasparini, F; Santini, S
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/394848
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