Literature DB >> 30441308

A Wrist-Worn Strap with an Array of Electrodes for Robust Physiological Sensing.

Bassem Ibrahim, Justin McMurray, And Roozbeh Jafari.   

Abstract

Robust sensing is one of the main challenges for wearable physiological monitoring because of the high dependency on the placement of electrodes on the body, retaining suitable contact between electrodes and skin, and the effect of motion artifacts. In this paper, we present a wrist-worn strap that includes a 2-D array of 48 miniature electrodes covering the bottom side of the wrist with good contact with the skin. Good skin contact directly impacts the sensing robustness. The array provides local measurements between adjacent electrodes that span the whole bottom side of the wrist with an area of 6.25×4.60 cm for robust sensing. The array allows for the automatic selection of the correct electrodes at the right location regardless of changes in the device placement on the wrist. In addition, using a large number of electrodes over a large area on the wrist ensures continuous contact of some electrodes with the skin during motion since all of the electrodes will not lose contact with the skin at the same time. We measured the electrode-skin impedance of the fabricated electrodes versus frequency and compared to other types of electrodes. We demonstrated good contact between all electrodes of the array and the skin by measuring electrode-skin impedance less than 10 k$\Omega$ at 16 kHz for all locations on the wrist strap. We also conducted measurements of impedance while the wearer was bending the wrist to validate the continuous contact of at least a subset of electrodes with the skin during such movements.

Entities:  

Mesh:

Year:  2018        PMID: 30441308      PMCID: PMC6626620          DOI: 10.1109/EMBC.2018.8513238

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  4 in total

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  4 in total
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4.  Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks.

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  4 in total

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