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Record W3025547549 · doi:10.1149/ma2020-01271901mtgabs

A Novel Electrophysiological Sensor

2020· article· en· W3025547549 on OpenAlexaff
Abhijith Balamuraleekrishna Shyam, Alexandra Cunningham, Aris Docoslis, Marianna Kontopoulou, Shideh Kabiri Ameri

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsElectrophysiologyBiomedical engineeringElectroencephalographyWearable computerMaterials scienceComputer scienceMedicineEmbedded systemNeurosciencePsychology

Abstract

fetched live from OpenAlex

Electrophysiological signals are electrical signals generated by different organs and tissues within the body like as brain, heart, muscles, etc.. These signals often contain information that can be utilized to access the physical and mental health status and therefore, have wide applications in medical and health care. [1] The non-invasive methods of measuring the electrical signal from brain, heart and muscles are known as electroencephalography (EEG), electrocardiography (ECG) and electromyography (EMG) respectively. Beyond medical applications electrophysiological recording has found various applications including human machine interface (HMI), mobile healthcare and internet of things (IoT). [2] Conventionally electrophysiological recording is performed using dry and wet gel electrodes. Besides being bulky and rigid wet gel electrodes are subject to drying by time and increasing skin-electrodes interface impedance, and dry electrodes are susceptible to motion artifacts because due to their slippage on skin during skin deformation. Therefore, their applications are limited to stationary and on-site medical care. Wearable, user-friendly sensors that can offer reliable signal recording during daily activities especially from hairy and microscopically rough skin such as scalp is an unaddressed problem. [3] Here we report a light weight, conductive polymer based, dry self-adhesive sensor (DSAS) for electrophysiological sensing from all-skin areas regardless of level of hair coverage and topology. DSAS contains of a low density array of funnel shaped structures (100/cm2). A single funnel shaped structure consist of a long stem (400-450 µm) and a micro-suction cup head (200-300 µm diameter) as it is shown in Figure 1.. The funnel shaped structures adhere to the skin when pressed against it due to pushing out the air and generating negative pressure inside the heads. Our theoretical studies suggest that one-centimeter square of DSAS can carry up to 2N force (200 gm). The long stem allows the adhesion of the DSAS to the hairy area as it can go between the hairs. The strong adhesion between the skin and sensor results firm and conformal contact to skin and reducing the skin-sensor interface impedance necessary for high signal to noise ratio signal recording. A novel low-cost and scalable fabrication method was developed for the fabrication of DSAS. To make electrically conductive polymer for the fabrication of DSAS a mixture of polydimethylsiloxane (PDMS) loaded with graphene and CNT at 3% of total weight (PDMS + CNT) is used for the fabrication of DSAS (Fig. 2 and 3). To achieve uniform distributions of CNTs within the polymer, an optimized dispersion process of CNT in PDMS was developed. It is found that exposure to an electric field yields CNT assembly into columnar structures parallel to the electric field (Fig. 4). A percolation threshold is observed at 3%, showing a dramatic increase from the neat polymer, and untreated polymer composite. The substrate is then molded into an array of funnel like micro-structure using a novel fabrication procedure, to allow self-adhesion to non-glabrous skin. The funnel shaped heads’ shell wall in the funnel like microstructure head is 15 µm thick which allows forming conformal contact to the rough surfaces such as skin and prevents leaking the air into the interface between sensor and surface. References [1] C. Im and J.-M. Seo, "A Review of Electrodes for the Electrical Brain Signal Recording," Biomedical Engineering Letters, 2016, 6: 104-112. [2] Y. Liu, M. Pharr and G. A. Salvatore, "Lab-on-Skin: A Review of Flexible and Stretchable Electronics for Wearable Health Monitoring," ACS Nano, 2017, 11: 9614−9635. [3] Kenry, J. Yeo and C. Lim, "Emerging flexible and wearable physical sensing platforms for healthcare and biomedical applications," Nature, 2016, 2: 16043. Figure 1

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.224
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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