Evidence map›Paper›PMID 41773224›Full record

Article... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks2025

mEDA: Mobile DC-EDA Circuit Validation.

Suparna Veeturi, Nishtha Bhagat, Vignesh Ravichandran, Ben Annicelli, Stephanie Carreiro, Krishna Venkatasubramanian, Dhaval Solanki, Kunal Mankodiya

Abstract read
In one paragraph

Article in ... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Suparna VeeturiDepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.
Nishtha BhagatDepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.
Vignesh RavichandranDepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.
Ben AnnicelliDepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.
Stephanie CarreiroDepartment of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Krishna VenkatasubramanianDepartment of Computer Science and Statistics, University of Rhode Island, Kingston, RI, USA.
Dhaval SolankiDepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.
Kunal MankodiyaDepartment of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.

Funding

MINDER: Wearable sensor-based detection of digital biomarkers of adherence to medications for opioid use disorderR01EB033581 · NIBIB · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI STEPHANIE P CARREIRO · 2023 to 2026
$2.6M
Mentoring in Patient Oriented Research for Digital Biomarker-Driven Solutions to prevent and Treat Opioid Use DisorderK24DA063801 · NIDA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI STEPHANIE P CARREIRO · 2025 to 2026
$289k
NIBIB NIH HHS R01 EB033581NIDA NIH HHS K24 DA063801
6 · The paper itself

Abstract

Electrodermal activity (EDA) provides a direct indicator of sympathetic nervous system arousal through changes in skin conductance. However, wearable EDA sensing poses challenges such as inconsistent skin contact, electrode impedance variability, motion artifacts, and power constraints. To address these issues, this study presents mobile EDA (mEDA), a compact device driven by a stabilized direct-current source. A validation study was conducted on ten healthy adult participants in a time-synchronized protocol to collect data from BIOPAC and mEDA concurrently. mEDA recordings employed gel electrodes for P1-P5 and dry (textile) electrodes for P6-P10, while the BIOPAC MP160 system used gel electrodes for all participants. Participants underwent a 30-minute protocol of resting, deep breathing, and three cognitive tasks. The preprocessing pipeline consisted of low-pass filter and artifact (sharp peaks and flat line) removal. Cleaned signals were converted into frequency domain components for decomposition into low and high frequency components, skin conductance level (SCL), and skin conductance response (SCR) respectively. SCL and SCR were converted back to the time domain to analyze performance metrics between both devices. Pearson correlation, coherence, and Dynamic Time Warping (DTW) were computed on SCL, while zero-crossing peaks were counted for SCR analysis. With gel electrodes, the average Pearson correlation was 0.92 and the SCR peak count difference was 38. For textile electrodes, the correlation was 0.88 with a peak count difference of 119. Both configurations achieved coherence above 0.95 and DTW below 0.5 for most participants. These results demonstrate mEDA's reliable performance in capturing both tonic and phasic EDA across electrode configurations.

Indexed as

BIOPACdry electrodeDynamic Time Warping (DTW)EDAphasicSCLSCRtonic

Identifiers

PMID41773224
PMCPMC12950208

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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 OpenQuestion graph.