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A Causal Analysis by Structural Equation Modeling of Sleep Monitoring Sensor Data

Nobuhiro Oishi, Naoki Yamamoto, Akio Ishida, and Jun Murakami
National Institute of Technology, 2659-2 Koshi-shi, Kumamoto, Japan
Abstract—In this paper, structural equation modeling (SEM) is used to analyze the causal relationship between the level of sleep and the environmental data. The data used for the analysis was obtained by a care support device used in an elderly care facility. By applying the stepwise selection method to this data, we were able to find four observation variables that affect the level of sleep. And the latent variables are determined by scree plot. We proposed a causal model in which four observed variables and two latent variables affect the level of sleep. Statistical analysis environment R and the lavaan package were used for SEM analysis in this paper. From this model, it was found that the indoor environment and the vital signs affect sleep, and that heart rate should be reduced to obtain deep sleep. 

Index Terms—sleep monitoring sensor, causal analysis, structural equation modeling, sleep level, R language

Cite: Nobuhiro Oishi, Naoki Yamamoto, Akio Ishida, and Jun Murakami, "A Causal Analysis by Structural Equation Modeling of Sleep Monitoring Sensor Data," International Journal of Electronics and Electrical Engineering, Vol. 8, No. 3, pp. 58-62, September 2020. doi: 10.18178/ijeee.8.3.58-62

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