Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring
Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the...
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MDPI AG
2024-11-01
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| author | Rinaldi Anwar Buyung Alhadi Bustamam Muhammad Remzy Syah Ramazhan |
| author_facet | Rinaldi Anwar Buyung Alhadi Bustamam Muhammad Remzy Syah Ramazhan |
| author_sort | Rinaldi Anwar Buyung |
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| description | Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood circulation cycle. However, this technique is sensitive to environmental lightning and different skin pigmentation, resulting in unreliable results. This research presents a multimodal approach to non-contact heart rate estimation by combining facial video and physical attributes, including age, gender, weight, height, and body mass index (BMI). For this purpose, we collected local datasets from 60 individuals containing a 1 min facial video and physical attributes such as age, gender, weight, and height, and we derived the BMI variable from the weight and height. We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. These findings highlight the potential for interpretable, non-contact, real-time heart rate measurement systems to contribute effectively to applications in telemedicine and mass screening. |
| format | Article |
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| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-dbaf2bc1ea0a46d4b2c66d2823bbd44f2025-08-20T01:55:27ZengMDPI AGSensors1424-82202024-11-012423753710.3390/s24237537Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate MonitoringRinaldi Anwar Buyung0Alhadi Bustamam1Muhammad Remzy Syah Ramazhan2Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok 16424, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok 16424, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok 16424, IndonesiaNon-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screening. Remote photoplethysmography (rPPG) is a non-contact technique to obtain information about heart pulse by analyzing the changes in the light intensity reflected or absorbed by the skin during the blood circulation cycle. However, this technique is sensitive to environmental lightning and different skin pigmentation, resulting in unreliable results. This research presents a multimodal approach to non-contact heart rate estimation by combining facial video and physical attributes, including age, gender, weight, height, and body mass index (BMI). For this purpose, we collected local datasets from 60 individuals containing a 1 min facial video and physical attributes such as age, gender, weight, and height, and we derived the BMI variable from the weight and height. We compare the performance of two machine learning models, support vector regression (SVR) and random forest regression on the multimodal dataset. The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. These findings highlight the potential for interpretable, non-contact, real-time heart rate measurement systems to contribute effectively to applications in telemedicine and mass screening.https://www.mdpi.com/1424-8220/24/23/7537heart ratemachine learningremote photoplethysmographysignal processing |
| spellingShingle | Rinaldi Anwar Buyung Alhadi Bustamam Muhammad Remzy Syah Ramazhan Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring Sensors heart rate machine learning remote photoplethysmography signal processing |
| title | Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring |
| title_full | Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring |
| title_fullStr | Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring |
| title_full_unstemmed | Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring |
| title_short | Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring |
| title_sort | integrating remote photoplethysmography and machine learning on multimodal dataset for noninvasive heart rate monitoring |
| topic | heart rate machine learning remote photoplethysmography signal processing |
| url | https://www.mdpi.com/1424-8220/24/23/7537 |
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