{"title":"CompRate: Power Efficient Heart Rate and Heart Rate Variability Monitoring on Smart Wearables","authors":"Vipula Dissanayake, Don Samitha Elvitigala, Haimo Zhang, Chamod Weerasinghe, Suranga Nanayakkara","doi":"10.1145/3359996.3364239","DOIUrl":null,"url":null,"abstract":"Currently, smartwatches are equipped with Photoplethysmography (PPG) sensors to measure Heart Rate (HR) and Heart Rate Variability (HRV). However, PPG sensors consume considerably high energy, making it impractical to monitor HR & HRV continuously for an extended period. Utilising low power accelerometers to estimate HR has been broadly discussed in previous decades. Inspired by prior work, we introduce CompRate, an alternative method to measure HR continuously for an extended period in low-intensity physical activities. CompRate model calibrated for individual users only has an average performance of Root Mean Squared Error (RMSE) 1.58 Beats Per Minute (BPM). Further, CompRate used 3.75 times less energy compared to the built-in PPG sensor. We also demonstrate that CompRate model can be extended to predict HRV. We will demonstrate CompRate in several application scenarios: self-awareness of fatigue and just-in-time interruption while driving; enabling teachers to be aware of students’ mental effort during a learning activity; and the broadcasting of the location of live victims in a disaster situation.","PeriodicalId":393864,"journal":{"name":"Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM Symposium on Virtual Reality Software and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3359996.3364239","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Currently, smartwatches are equipped with Photoplethysmography (PPG) sensors to measure Heart Rate (HR) and Heart Rate Variability (HRV). However, PPG sensors consume considerably high energy, making it impractical to monitor HR & HRV continuously for an extended period. Utilising low power accelerometers to estimate HR has been broadly discussed in previous decades. Inspired by prior work, we introduce CompRate, an alternative method to measure HR continuously for an extended period in low-intensity physical activities. CompRate model calibrated for individual users only has an average performance of Root Mean Squared Error (RMSE) 1.58 Beats Per Minute (BPM). Further, CompRate used 3.75 times less energy compared to the built-in PPG sensor. We also demonstrate that CompRate model can be extended to predict HRV. We will demonstrate CompRate in several application scenarios: self-awareness of fatigue and just-in-time interruption while driving; enabling teachers to be aware of students’ mental effort during a learning activity; and the broadcasting of the location of live victims in a disaster situation.