A Novel Electrophysiological Sensor
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».