An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts
The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/13/7/718 |
| _version_ | 1797590005474918400 |
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| author | José María Vicente-Samper Christian Tamantini Ernesto Ávila-Navarro Miguel Ángel De La Casa-Lillo Loredana Zollo José María Sabater-Navarro Francesca Cordella |
| author_facet | José María Vicente-Samper Christian Tamantini Ernesto Ávila-Navarro Miguel Ángel De La Casa-Lillo Loredana Zollo José María Sabater-Navarro Francesca Cordella |
| author_sort | José María Vicente-Samper |
| collection | DOAJ |
| description | The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user’s HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG. |
| first_indexed | 2024-03-11T01:15:12Z |
| format | Article |
| id | doaj.art-08d24df308da4278ad823669f890aae8 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-11T01:15:12Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-08d24df308da4278ad823669f890aae82023-11-18T18:33:02ZengMDPI AGBiosensors2079-63742023-07-0113771810.3390/bios13070718An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion ArtifactsJosé María Vicente-Samper0Christian Tamantini1Ernesto Ávila-Navarro2Miguel Ángel De La Casa-Lillo3Loredana Zollo4José María Sabater-Navarro5Francesca Cordella6Neuroengineering Biomedical Group, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, SpainUnit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, ItalyDepartment of Materials Science, Optics and Electronic Technology, Miguel Hernández University of Elche, 03202 Elche, SpainInstitute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, SpainUnit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, ItalyNeuroengineering Biomedical Group, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, SpainUnit of Advanced Robotics and Human-Centred Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, ItalyThe heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user’s HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.https://www.mdpi.com/2079-6374/13/7/718heart ratephotopletismographymotion artifactsartificial intelligence |
| spellingShingle | José María Vicente-Samper Christian Tamantini Ernesto Ávila-Navarro Miguel Ángel De La Casa-Lillo Loredana Zollo José María Sabater-Navarro Francesca Cordella An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts Biosensors heart rate photopletismography motion artifacts artificial intelligence |
| title | An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts |
| title_full | An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts |
| title_fullStr | An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts |
| title_full_unstemmed | An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts |
| title_short | An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts |
| title_sort | ml based approach to reconstruct heart rate from ppg in presence of motion artifacts |
| topic | heart rate photopletismography motion artifacts artificial intelligence |
| url | https://www.mdpi.com/2079-6374/13/7/718 |
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