Exploring Bio-Impedance Sensing for Intelligent Wearable Devices
I tiakina i:
| I whakaputaina i: | Bioengineering vol. 12, no. 5 (2025), p. 521 |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | , , |
| I whakaputaina: |
MDPI AG
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
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| 022 | |a 2306-5354 | ||
| 024 | 7 | |a 10.3390/bioengineering12050521 |2 doi | |
| 035 | |a 3211860280 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Arabsalmani Nafise |u School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 14395-515, Iran; n.a.salmani@ut.ac.ir (N.A.); sashtiani@ut.ac.ir (S.J.A.) | |
| 245 | 1 | |a Exploring Bio-Impedance Sensing for Intelligent Wearable Devices | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The rapid growth of wearable technology has opened new possibilities for smart health-monitoring systems. Among various sensing methods, bio-impedance sensing has stood out as a powerful, non-invasive, and energy-efficient way to track physiological changes and gather important health information. This review looks at the basic principles behind bio-impedance sensing, how it is being built into wearable devices, and its use in healthcare and everyday wellness tracking. We examine recent progress in sensor design, signal processing, and machine learning, and show how these developments are making real-time health monitoring more effective. While bio-impedance systems offer many advantages, they also face challenges, particularly when it comes to making devices smaller, reducing power use, and improving the accuracy of collected data. One key issue is that analyzing bio-impedance signals often relies on complex digital signal processing, which can be both computationally heavy and energy-hungry. To address this, researchers are exploring the use of neuromorphic processors—hardware inspired by the way the human brain works. These processors use spiking neural networks (SNNs) and event-driven designs to process signals more efficiently, allowing bio-impedance sensors to pick up subtle physiological changes while using far less power. This not only extends battery life but also brings us closer to practical, long-lasting health-monitoring solutions. In this paper, we aim to connect recent engineering advances with real-world applications, highlighting how bio-impedance sensing could shape the next generation of intelligent wearable devices. | |
| 653 | |a Physiology | ||
| 653 | |a Tomography | ||
| 653 | |a Flow velocity | ||
| 653 | |a Epilepsy | ||
| 653 | |a Brain research | ||
| 653 | |a Heart failure | ||
| 653 | |a Chronic illnesses | ||
| 653 | |a Wearable technology | ||
| 653 | |a Measurement techniques | ||
| 653 | |a Neurosciences | ||
| 653 | |a Machine learning | ||
| 653 | |a Hydration | ||
| 653 | |a Breast cancer | ||
| 653 | |a Marking and tracking techniques | ||
| 653 | |a Signal processing | ||
| 653 | |a Impedance | ||
| 653 | |a Neural networks | ||
| 653 | |a Spectrum analysis | ||
| 653 | |a Digital signal processing | ||
| 653 | |a Blood pressure | ||
| 653 | |a Edema | ||
| 653 | |a Firing pattern | ||
| 653 | |a Power management | ||
| 653 | |a Wearable computers | ||
| 653 | |a Hemodynamics | ||
| 653 | |a Biomarkers | ||
| 653 | |a Devices | ||
| 653 | |a Body composition | ||
| 653 | |a Processors | ||
| 653 | |a Tissues | ||
| 653 | |a Brain diseases | ||
| 653 | |a Real time | ||
| 653 | |a Ischemia | ||
| 653 | |a Ultrasonic imaging | ||
| 653 | |a Traumatic brain injury | ||
| 653 | |a Heart rate | ||
| 700 | 1 | |a Ghouchani Arman |u Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark; aghouchani@ece.au.dk | |
| 700 | 1 | |a Shahin, Jafarabadi Ashtiani |u School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 14395-515, Iran; n.a.salmani@ut.ac.ir (N.A.); sashtiani@ut.ac.ir (S.J.A.) | |
| 700 | 1 | |a Zamani Milad |u Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark; aghouchani@ece.au.dk | |
| 773 | 0 | |t Bioengineering |g vol. 12, no. 5 (2025), p. 521 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211860280/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211860280/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211860280/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |