Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications
A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for elec...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/11/12/1252 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846105775853797376 |
|---|---|
| author | Jegan Rajendran Nimi Wilson Sukumari P. Subha Hency Jose Manikandan Rajendran Manob Jyoti Saikia |
| author_facet | Jegan Rajendran Nimi Wilson Sukumari P. Subha Hency Jose Manikandan Rajendran Manob Jyoti Saikia |
| author_sort | Jegan Rajendran |
| collection | DOAJ |
| description | A battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost. |
| format | Article |
| id | doaj-art-758050f2fbf7413e9e0a96b4ccebd27d |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-758050f2fbf7413e9e0a96b4ccebd27d2024-12-27T14:11:38ZengMDPI AGBioengineering2306-53542024-12-011112125210.3390/bioengineering11121252Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare ApplicationsJegan Rajendran0Nimi Wilson Sukumari1P. Subha Hency Jose2Manikandan Rajendran3Manob Jyoti Saikia4Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USABiomedical Engineering Department, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, IndiaBiomedical Engineering Department, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, IndiaElectrical Engineering Department, Einstein College of Engineering, Tirunelveli 627012, Ramil Nadu, IndiaBiomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USAA battery-operated biomedical wearable device gradually assists in clinical tasks to monitor patients’ health states regarding early diagnosis and detection. This paper presents the development of a self-powered portable electronic module by integrating an onboard energy-harvesting facility for electrocardiogram (ECG) signal processing and personalized health monitoring. The developed electronic module provides a customizable approach to power the device using a lithium-ion battery, a series of silicon photodiode arrays, and a solar panel. The new architecture and techniques offered by the developed method include an analog front-end unit, a signal processing unit, and a battery management unit for the acquiring and processing of real-time ECG signals. The dynamic multi-level wavelet packet decomposition framework has been used and applied to an ECG signal to extract the desired features by removing overlapped and repeated samples from an ECG signal. Further, a random forest with deep decision tree (RFDDT) architecture has been designed for offline ECG signal classification, and experimental results provide the highest accuracy of 99.72%. One assesses the custom-developed sensor by comparing its data with those of conventional biosensors. The onboard energy-harvesting and battery management circuits are designed with a BQ25505 microprocessor with the support of silicon photodiodes and solar cells which detect the ambient light variations and provide a maximum of 4.2 V supply to enable the continuous operation of an entire module. The measurements conducted on each unit of the proposed method demonstrate that the proposed signal-processing method significantly reduces the overlapping samples from the raw ECG data and the timing requirement criteria for personalized and wearable health monitoring. Also, it improves temporal requirements for ECG data processing while achieving excellent classification performance at a low computing cost.https://www.mdpi.com/2306-5354/11/12/1252biosensorECG signal processingfeature extractionenergy harvestingmachine learning algorithmsphysiological vital parameters |
| spellingShingle | Jegan Rajendran Nimi Wilson Sukumari P. Subha Hency Jose Manikandan Rajendran Manob Jyoti Saikia Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications Bioengineering biosensor ECG signal processing feature extraction energy harvesting machine learning algorithms physiological vital parameters |
| title | Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications |
| title_full | Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications |
| title_fullStr | Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications |
| title_full_unstemmed | Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications |
| title_short | Development of Self-Powered Energy-Harvesting Electronic Module and Signal-Processing Framework for Wearable Healthcare Applications |
| title_sort | development of self powered energy harvesting electronic module and signal processing framework for wearable healthcare applications |
| topic | biosensor ECG signal processing feature extraction energy harvesting machine learning algorithms physiological vital parameters |
| url | https://www.mdpi.com/2306-5354/11/12/1252 |
| work_keys_str_mv | AT jeganrajendran developmentofselfpoweredenergyharvestingelectronicmoduleandsignalprocessingframeworkforwearablehealthcareapplications AT nimiwilsonsukumari developmentofselfpoweredenergyharvestingelectronicmoduleandsignalprocessingframeworkforwearablehealthcareapplications AT psubhahencyjose developmentofselfpoweredenergyharvestingelectronicmoduleandsignalprocessingframeworkforwearablehealthcareapplications AT manikandanrajendran developmentofselfpoweredenergyharvestingelectronicmoduleandsignalprocessingframeworkforwearablehealthcareapplications AT manobjyotisaikia developmentofselfpoweredenergyharvestingelectronicmoduleandsignalprocessingframeworkforwearablehealthcareapplications |