# Integrating Mobile Devices and Wearable Technology for Optimal Sleep Conditions
## 4 Conclusions  
Prior research has shown that collecting physiological data via polysomnography can lead to wearer discomfort during sleep [53; 54; 55]. In light of this, our study introduces an algorithmic analysis module tailored for the interpretation of physiological data harnessed from intelligent wearable devices. This module harmonizes the acquired physiological data from smart wearables with closely correlated data derived from polysomnography, thereby delineating sleep stages. The primary focus of this investigation revolves around the enhancement of sleep quality, encompassing the following key facets:  
1. A novel sleep state algorithm is proffered for interpreting sleep stages using physiological signals from smart wearable devices. The primary objective is to mitigate the reliance on conventional physiological signal acquisition instruments that necessitate direct physical contact with the body.
2. Through a comprehensive analysis of cloud-based databases and the uploading of personalized physiological data, our study presents an iterative updating mechanism for personalized and precise sleep algorithms. Each set of individual physiological data is subjected to a secondary analysis compared to the original dataset, facilitating the calibration of individual physiological data algorithms. These refinements consider variations in physiological markers during periods of inactivity as well as the frequency of bodily movements.
3. Our research extends beyond prior endeavors in dynamic sleep-inducing illumination [50; 56; 57; 58], aiming to minimize undue physiological data acquisition and re-shape the paradigm of physiological data analysis from smart wearable devices. Prospective  
Figure 11: HRV analysis results of heartbeat data uploaded by users.  
work entails utilizing physiological data harnessed by wearable devices to conduct sleep state analyses, enabling refinements to dynamic sleep-inducing illumination, and amplifying users' sleep efficiency.  
Methodology, Y.-K.W. and C.-Y.C.; Formal analysis, Y.-K.W.; Investigation, C.-Y.C.; Data curation, Y.-K.W.; Writing--original draft, Y.-K.W. and C.-Y.C.; Project administration, C.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council of Taiwan under contract No. 112-2218-E-011-005-MBK.  
This study obtained approval from the Institutional Review Board of National Taiwan University for conducting a human factor experiment. The experiment was granted approval under the reference number 202007 EM041.  
Informed Consent Statement: Informed consent was obtained from all volunteers involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.  
All data generated or analyzed to support the findings of the present study are included this article. The raw data can be obtained from the authors, upon reasonable request.  
We thank the technical consulting of OSENCE Technology and [PERSON] & [PERSON] for performing the studies.  
The authors declare no conflict of interest.