Healthcare Monitoring Using an Internet of Things-Based Cardio System
This study describes an IoT-based health monitoring system designed to notify attending physicians when necessary. The developed IoT system incorporates sensors for ECG, PPG, and temperature; a gyroscope/accelerometer; and a microcontroller. A critical analysis of existing components in these areas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | IoT |
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| Online Access: | https://www.mdpi.com/2624-831X/6/1/10 |
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| author | Galya Georgieva-Tsaneva Krasimir Cheshmedzhiev Yoan-Aleksandar Tsanev Miroslav Dechev Ekaterina Popovska |
| author_facet | Galya Georgieva-Tsaneva Krasimir Cheshmedzhiev Yoan-Aleksandar Tsanev Miroslav Dechev Ekaterina Popovska |
| author_sort | Galya Georgieva-Tsaneva |
| collection | DOAJ |
| description | This study describes an IoT-based health monitoring system designed to notify attending physicians when necessary. The developed IoT system incorporates sensors for ECG, PPG, and temperature; a gyroscope/accelerometer; and a microcontroller. A critical analysis of existing components in these areas was conducted to ensure the IoT system’s good performance, reliability, and suitability for continuous cardiac monitoring and data processing. This study addresses the challenge of monitoring cardiac activity in patients with arrhythmias, focusing on the differences in heart rate variability (HRV) parameters between healthy individuals and those with extrasystolic arrhythmia. The purpose of this research is to evaluate the effectiveness of IoT-based systems using PPG and ECG sensors for cardiac data registration and HRV analysis. The system leverages time domain and frequency domain methods for HRV analysis to assess the states of the autonomic nervous system. Significant differences were observed in HRV parameters, such as the SDNN, SDANN, RMSSD, and the LF/HF ratio. The results demonstrated that both the PPG and ECG methods provide comparable HRV measurements, despite PPG’s higher susceptibility to noise. This study concludes that IoT-based monitoring systems with PPG and ECG integration can reliably detect arrhythmias and offer real-time data for cardiac care. |
| format | Article |
| id | doaj-art-baf8a73e22db4e9b830cc50be659b8e7 |
| institution | Kabale University |
| issn | 2624-831X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | IoT |
| spelling | doaj-art-baf8a73e22db4e9b830cc50be659b8e72025-08-20T03:43:11ZengMDPI AGIoT2624-831X2025-02-01611010.3390/iot6010010Healthcare Monitoring Using an Internet of Things-Based Cardio SystemGalya Georgieva-Tsaneva0Krasimir Cheshmedzhiev1Yoan-Aleksandar Tsanev2Miroslav Dechev3Ekaterina Popovska4Institute of Robotics, Bulgarian Academy of Science, 1113 Sofia, BulgariaInstitute of Robotics, Bulgarian Academy of Science, 1113 Sofia, BulgariaFaculty of Computing and Automation, Technical University of Varna, 9010 Varna, BulgariaInstitute of Robotics, Bulgarian Academy of Science, 1113 Sofia, BulgariaInstitute of Robotics, Bulgarian Academy of Science, 1113 Sofia, BulgariaThis study describes an IoT-based health monitoring system designed to notify attending physicians when necessary. The developed IoT system incorporates sensors for ECG, PPG, and temperature; a gyroscope/accelerometer; and a microcontroller. A critical analysis of existing components in these areas was conducted to ensure the IoT system’s good performance, reliability, and suitability for continuous cardiac monitoring and data processing. This study addresses the challenge of monitoring cardiac activity in patients with arrhythmias, focusing on the differences in heart rate variability (HRV) parameters between healthy individuals and those with extrasystolic arrhythmia. The purpose of this research is to evaluate the effectiveness of IoT-based systems using PPG and ECG sensors for cardiac data registration and HRV analysis. The system leverages time domain and frequency domain methods for HRV analysis to assess the states of the autonomic nervous system. Significant differences were observed in HRV parameters, such as the SDNN, SDANN, RMSSD, and the LF/HF ratio. The results demonstrated that both the PPG and ECG methods provide comparable HRV measurements, despite PPG’s higher susceptibility to noise. This study concludes that IoT-based monitoring systems with PPG and ECG integration can reliably detect arrhythmias and offer real-time data for cardiac care.https://www.mdpi.com/2624-831X/6/1/10internet of thingshealth monitoringwearable devicessensorsheart rate variabilitytelemedicine |
| spellingShingle | Galya Georgieva-Tsaneva Krasimir Cheshmedzhiev Yoan-Aleksandar Tsanev Miroslav Dechev Ekaterina Popovska Healthcare Monitoring Using an Internet of Things-Based Cardio System IoT internet of things health monitoring wearable devices sensors heart rate variability telemedicine |
| title | Healthcare Monitoring Using an Internet of Things-Based Cardio System |
| title_full | Healthcare Monitoring Using an Internet of Things-Based Cardio System |
| title_fullStr | Healthcare Monitoring Using an Internet of Things-Based Cardio System |
| title_full_unstemmed | Healthcare Monitoring Using an Internet of Things-Based Cardio System |
| title_short | Healthcare Monitoring Using an Internet of Things-Based Cardio System |
| title_sort | healthcare monitoring using an internet of things based cardio system |
| topic | internet of things health monitoring wearable devices sensors heart rate variability telemedicine |
| url | https://www.mdpi.com/2624-831X/6/1/10 |
| work_keys_str_mv | AT galyageorgievatsaneva healthcaremonitoringusinganinternetofthingsbasedcardiosystem AT krasimircheshmedzhiev healthcaremonitoringusinganinternetofthingsbasedcardiosystem AT yoanaleksandartsanev healthcaremonitoringusinganinternetofthingsbasedcardiosystem AT miroslavdechev healthcaremonitoringusinganinternetofthingsbasedcardiosystem AT ekaterinapopovska healthcaremonitoringusinganinternetofthingsbasedcardiosystem |