Abstract
People have been increasingly interested in sleep, and the number of services that provide simple sleep status monitoring is increasing. In order for users to continue to use these services, it is necessary to ensure that both users and services grow together. To achieve this, it is important to provide sleep state that is acceptable for people. Toward personalized sleep stage estimation, this paper proposes Correction of Estimation Results based on the Time Series Probability of Estimation for each label. The method focuses the case that the probability of estimation is small and unlikely to be positive, but it is relatively larger than the probabilities of other times considering the before and after the epoch, and corrects the estimation by machine learning. For the machine learning, this paper employs Random Forest. And, the probability of the estimation is calculated from estimations by each decision tree in RF. Through the human subject experiments, the next implication has been revealed. The proposed method can reduce the number of wrong REM sleep estimates without significantly reducing the agreement rate and the number of estimated correct REM sleeps. This suggests that the proposed sleep stage estimation method can provide the possibility to improve the agreement by using estimation information (probability) of user.
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Nakari, I., Nakashima, M., Takadama, K. (2023). Personalized Sleep Stage Estimation Based on Time Series Probability of Estimation for Each Label with Wearable 3-Axis Accelerometer. In: Mori, H., Asahi, Y. (eds) Human Interface and the Management of Information. HCII 2023. Lecture Notes in Computer Science, vol 14015. Springer, Cham. https://doi.org/10.1007/978-3-031-35132-7_40
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DOI: https://doi.org/10.1007/978-3-031-35132-7_40
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