Evidence map›Paper›PMID 40428140›Full record

ArticleBioengineering (Basel, Switzerland)2025

Exploring Bio-Impedance Sensing for Intelligent Wearable Devices.

Nafise Arabsalmani, Arman Ghouchani, Shahin Jafarabadi Ashtiani, Milad Zamani

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Advances in Wearable Bioimaging.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  4. Review
  5. Review
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nafise ArabsalmaniSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 14395-515, Iran.ORCID 0009-0008-1077-986X
Arman GhouchaniDepartment of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark.ORCID 0009-0002-9840-6174
Shahin Jafarabadi AshtianiSchool of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 14395-515, Iran.ORCID 0000-0002-5710-405X
Milad ZamaniDepartment of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark.ORCID 0000-0002-8718-7095

Funding

HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) 101118964
6 · The paper itself

Abstract

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.

Indexed as

bio-impedanceneuromorphic computingphysiological changeswearable devices

Identifiers

PMID40428140
PMCPMC12109311

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.