A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals
Dynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estim...
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| Format: | Article |
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MDPI AG
2017-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/11/2450 |
| _version_ | 1811305556263043072 |
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| author | Jiajia Song Dan Li Xiaoyuan Ma Guowei Teng Jianming Wei |
| author_facet | Jiajia Song Dan Li Xiaoyuan Ma Guowei Teng Jianming Wei |
| author_sort | Jiajia Song |
| collection | DOAJ |
| description | Dynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estimation algorithm framework that uses one-channel PPG and tri-axis acceleration data to reconstruct the PPG and calculate the HR based on features of the PPG and spectral analysis. Firstly, the signal is judged by the presence of MAs. Then, the spectral peaks corresponding to acceleration data are filtered from the periodogram of the PPG when MAs exist. Different signal-processing methods are applied based on the amount of remaining PPG spectral peaks. The main MA-removal algorithm (NFEEMD) includes the repeated single-notch filter and ensemble empirical mode decomposition. Finally, HR calibration is designed to ensure the accuracy of HR tracking. The NFEEMD algorithm was performed on the 23 datasets from the 2015 IEEE Signal Processing Cup Database. The average estimation errors were 1.12 BPM (12 training datasets), 2.63 BPM (10 testing datasets) and 1.87 BPM (all 23 datasets), respectively. The Pearson correlation was 0.992. The experiment results illustrate that the proposed algorithm is not only suitable for HR estimation during continuous activities, like slow running (13 training datasets), but also for intense physical activities with acceleration, like arm exercise (10 testing datasets). |
| first_indexed | 2024-04-13T08:27:28Z |
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| id | doaj.art-0346f25ed629498ab635d0e5ba761db6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:27:28Z |
| publishDate | 2017-10-01 |
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| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0346f25ed629498ab635d0e5ba761db62022-12-22T02:54:21ZengMDPI AGSensors1424-82202017-10-011711245010.3390/s17112450s17112450A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic SignalsJiajia Song0Dan Li1Xiaoyuan Ma2Guowei Teng3Jianming Wei4School of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaDynamic accurate heart-rate (HR) estimation using a photoplethysmogram (PPG) during intense physical activities is always challenging due to corruption by motion artifacts (MAs). It is difficult to reconstruct a clean signal and extract HR from contaminated PPG. This paper proposes a robust HR-estimation algorithm framework that uses one-channel PPG and tri-axis acceleration data to reconstruct the PPG and calculate the HR based on features of the PPG and spectral analysis. Firstly, the signal is judged by the presence of MAs. Then, the spectral peaks corresponding to acceleration data are filtered from the periodogram of the PPG when MAs exist. Different signal-processing methods are applied based on the amount of remaining PPG spectral peaks. The main MA-removal algorithm (NFEEMD) includes the repeated single-notch filter and ensemble empirical mode decomposition. Finally, HR calibration is designed to ensure the accuracy of HR tracking. The NFEEMD algorithm was performed on the 23 datasets from the 2015 IEEE Signal Processing Cup Database. The average estimation errors were 1.12 BPM (12 training datasets), 2.63 BPM (10 testing datasets) and 1.87 BPM (all 23 datasets), respectively. The Pearson correlation was 0.992. The experiment results illustrate that the proposed algorithm is not only suitable for HR estimation during continuous activities, like slow running (13 training datasets), but also for intense physical activities with acceleration, like arm exercise (10 testing datasets).https://www.mdpi.com/1424-8220/17/11/2450photoplethysmographymotion artifactsheart rate estimationintense physical activitiesthe single notch filterEEMD |
| spellingShingle | Jiajia Song Dan Li Xiaoyuan Ma Guowei Teng Jianming Wei A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals Sensors photoplethysmography motion artifacts heart rate estimation intense physical activities the single notch filter EEMD |
| title | A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals |
| title_full | A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals |
| title_fullStr | A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals |
| title_full_unstemmed | A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals |
| title_short | A Robust Dynamic Heart-Rate Detection Algorithm Framework During Intense Physical Activities Using Photoplethysmographic Signals |
| title_sort | robust dynamic heart rate detection algorithm framework during intense physical activities using photoplethysmographic signals |
| topic | photoplethysmography motion artifacts heart rate estimation intense physical activities the single notch filter EEMD |
| url | https://www.mdpi.com/1424-8220/17/11/2450 |
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