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http://dx.doi.org/10.3745/KTSDE.2017.6.2.91

Feedback-RFC Model to Individualize Heartbeat Standards  

Kim, Taehyun (KAIST 전산학)
Jung, Pilsu (KAIST 전산학)
Lee, Seonah (경상대학교 항공우주 및 소프트웨어공학전공)
Chung, Ki-Sook (한국전자통신연구원)
Keum, Changsup (한국전자통신연구원 신뢰통신서비스플랫폼연구실)
Kang, Sungwon (KAIST 전산학)
Publication Information
KIPS Transactions on Software and Data Engineering / v.6, no.2, 2017 , pp. 91-102 More about this Journal
Abstract
Many of the wearable smart fitness devices provide services related to users' heartbeat rates. These services use fixed standards which have been pre-determined based on statistical data. However, because body conditions significantly differ between individuals, the services applying fixed standards to all individuals are not reliable. This paper proposes the Feedback-RFC model which adapts heartbeat standards to individual users' exercise abilities and also proposes a method to implement the model. This paper also shows the effectiveness of the Feedback-RFC model by collecting heartbeat data from 12 participants and evaluating the model with the data.
Keywords
Smart Fitness; Machine Learning; Feedback;
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