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%.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Electronics, Information and Communication, ICEIC 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-2 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781538647547 |
| DOIs | |
| State | Published - 2 Apr 2018 |
| Event | 17th International Conference on Electronics, Information and Communication, ICEIC 2018 - Honolulu, United States Duration: 24 Jan 2018 → 27 Jan 2018 |
Publication series
| Name | International Conference on Electronics, Information and Communication, ICEIC 2018 |
|---|---|
| Volume | 2018-January |
Conference
| Conference | 17th International Conference on Electronics, Information and Communication, ICEIC 2018 |
|---|---|
| Country/Territory | United States |
| City | Honolulu |
| Period | 24/01/18 → 27/01/18 |
Bibliographical note
Publisher Copyright:© 2018 Institute of Electronics and Information Engineers.
Keywords
- Fitbit data
- HMM
- body state
- forecasting
- heart rate