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http://dx.doi.org/10.3745/KTCCS.2019.8.4.93

Exercise Detection Method by Using Heart Rate and Activity Intensity in Wrist-Worn Device  

Sung, Ji Hoon (아주대학교 전자공학과)
Choi, Sun Tak (아주대학교 전자공학과)
Lee, Joo Young (아주대학교 전자공학과)
Cho, We-Duke (아주대학교 전자공학과)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.8, no.4, 2019 , pp. 93-102 More about this Journal
Abstract
As interest in wellness grows, There is a lot of research about monitoring individual health using wearable devices. Accordingly, a variety of methods have been studied to distinguish exercise from daily activities using wearable devices. Most of these existing studies are machine learning methods. However, there are problems with over-fitting on individual person's learning, data discontinuously recognition by independent segmenting and fake activity. This paper suggests a detection method for exercise activity based on the physiological response principle of heart rate up and down during exercise. This proposed method calculates activity intensity and heart rate from triaxial and photoplethysmography sensor to determine a heart rate recovery, then detects exercise by estimating activity intensity or detecting a heart rate rising state. Experimental results show that our proposed algorithm has 98.64% of averaged accuracy, 98.05% of averaged precision and 98.62% of averaged recall.
Keywords
Wearable Device; Exercise Detection; Physiological Principal; Physiological Signal; Activity Recognition;
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