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• W4281749874 abstract "Abstract Introduction Sleep is associated with temperature changes in the human body. At sleep onset, distal (hands and feet) and proximal (abdomen) temperatures increase by ~1°C and ~0.5°C, respectively, forming a distal-to-proximal gradient that increases throughout the first half of a night’s sleep. However, core temperature decreases during this period. Few devices can measure these temperatures unobtrusively. Our aim was to estimate distal skin temperature unobtrusively during sleep using a temperature sensor array on a mattress. Methods Three male volunteers participated in the study. Skin temperatures were measured using an array of 5 equally spaced thermistors distributed laterally across the mattress region aligning with the torso. Participants wore an Empatica smart watch to provide benchmark distal skin temperatures.Data from 249 sleep hours were used to build predictive models estimating distal skin temperature. The preprocessed data were grouped by participant and segmented into training and test sets (~60%/40%, respectively), with earlier sleep sessions selected for training and later sessions selected for testing. Using the Automated Machine Learning (AutoML) feature in Databricks, a model was developed that optimized R2. This model was applied to the test set and its performance evaluated by Bland-Altman analysis, using predicted and benchmark distal temperatures. Results The AutoML selected an XGBoost decision-tree model to predict distal skin temperature for each minute. The mean difference in temperature between predicted and benchmark readings was 0.13°C (R2=0.35), with lower and upper limits of agreement (LOA) of -1.59 and 1.84, respectively. Next, all minute-level data were averaged by sleep session, and model performance was re-evaluated across all sleep sessions. This approach resulted in smaller LOAs (-0.58, 0.91), with a mean difference in temperature of 0.16°C (R2=0.73). Conclusion Our results suggest that a temperature sensor array, coupled with an optimized decision-tree model, can predict mean distal skin temperature for each sleep session with reasonable accuracy. Our system enabled unobtrusive, ecologically valid collection of distal skin temperatures during sleep and may be useful for future studies of overnight temperature dynamics. Support (If Any) This study was funded by Sleep Number Corporation. Medical writing support, by Sandra J. Page, PhD, Oxford PharmaGenesis Inc., was funded by Sleep Number Corporation." @default.
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• W4281749874 date "2022-05-25" @default.
• W4281749874 modified "2023-09-27" @default.
• W4281749874 title "0086 Unobtrusive Sensing of Skin Temperature During Sleep Using a Mattress Sensor" @default.
• W4281749874 doi "https://doi.org/10.1093/sleep/zsac079.084" @default.
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