{"title":"SleepSensei: An Automated Sleep Quality Monitor and Sleep Duration Estimator","authors":"Amrith Krishna, Madhumita Mallick, Bivas Mitra","doi":"10.1145/2933566.2933570","DOIUrl":null,"url":null,"abstract":"SleepSensei is an automated sleep quality monitor and estimates the sleep duration for a user. It essentially builds a personalized model for determining the apt sleep duration for a given user. In our model, with the help of multiple inter-connected devices, we combine features related to user surroundings and those related to user movements during sleep. We use regression models in our system to calculate \"sleep share\" of a user, and report an MSE as low as 0.0041 when tested on 7 users, with more than a week's data. We also develop a smart alarm based on the notion of \"sleep quota\" from the model. The alarm has an average precision of 0.87 from the survey conducted. We also present an in-depth feature analysis to give further insights of individual sleep behavior.","PeriodicalId":292301,"journal":{"name":"Proceedings of the First Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2933566.2933570","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
SleepSensei is an automated sleep quality monitor and estimates the sleep duration for a user. It essentially builds a personalized model for determining the apt sleep duration for a given user. In our model, with the help of multiple inter-connected devices, we combine features related to user surroundings and those related to user movements during sleep. We use regression models in our system to calculate "sleep share" of a user, and report an MSE as low as 0.0041 when tested on 7 users, with more than a week's data. We also develop a smart alarm based on the notion of "sleep quota" from the model. The alarm has an average precision of 0.87 from the survey conducted. We also present an in-depth feature analysis to give further insights of individual sleep behavior.