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Forecasting of heart rate variability using wrist-worn heart rate monitor based on hidden Markov model

Title
Forecasting of heart rate variability using wrist-worn heart rate monitor based on hidden Markov model
Authors
Yun, Sang HunSon, Chang SikLee, Sang-HoKang, Won-Seok
DGIST Authors
Son, Chang Sik; Kang, Won-Seok
Issue Date
2018-01-24
Citation
17th International Conference on Electronics, Information and Communication, ICEIC 2018, 1-2
Type
Conference
ISBN
9781538647547
Abstract
In this paper, we present Hidden Markov Models (HMM) approach for forecasting the changes of heart rate. Heart rate is an important indicator of the state of our body. Forecasting changes of heart rate is equivalent to forecasting changes of the body state. We use numerous HMM models that is trained by datasets clustered on similarity basis. We find the optimal models with best probabilities in various learned HMM models and use this model to predict next heart rate variability. The heart rate data are collected by Fitbit-HR from 190 healthy persons. The prediction performance was accuracy = 91.87% and recall = 91.67%. © 2018 Institute of Electronics and Information Engineers.
URI
http://hdl.handle.net/20.500.11750/6674
DOI
10.23919/ELINFOCOM.2018.8330626
Publisher
Institute of Electrical and Electronics Engineers Inc.
Related Researcher
  • Author Kang, Won-Seok  
  • Research Interests Data Mining & Machine Learning for Text & Multimedia, Brain-Sense-ICTConvergence Computing, Computational Olfaction Measurement, Simulation&Modeling
Files:
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Collection:
Convergence Research Center for Wellness2. Conference Papers


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