@Article{info:doi/10.2196/18370，作者=“刘嘉兴与赵，杨与赖，博雅与王，海良与徐国梁”，标题=“基于无监督方法的可穿戴设备心率和活动数据个性化睡眠监测:算法验证”，期刊=“JMIR Mhealth Uhealth”，年=“2020”，月=“8”，日=“5”，卷=“8”，号=“8”，页=“e18370”，关键词=“睡眠/觉醒识别”;隐马尔可夫模型;个性化的健康;无监督学习;睡眠;身体活动;衣物;背景:收集活动和心率数据的可穿戴设备的激增，促进了不引人注目和纵向测量睡眠和清醒持续时间的新方法。大多数现有的睡眠/觉醒识别算法仅基于活动，并且在昂贵且费力的标注多导睡眠图(PSG)上进行训练。心率也可以反映睡眠/清醒的转变，这促使了本文在无监督算法中的研究。 Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns. Objective: We aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach's output with that of an existing commercial wearable device's algorithms. Methods: In this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual's data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified. Results: When applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants. Conclusions: Our unsupervised approach can be effectively implemented based on an individual's multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters. ", issn="2291-5222", doi="10.2196/18370", url="https://mhealth.www.mybigtv.com/2020/8/e18370", url="https://doi.org/10.2196/18370", url="http://www.ncbi.nlm.nih.gov/pubmed/32755887" }