Remotely Powered Two-Wire Cooperative Sensors for Biopotential Imaging Wearables

dc.contributor.authorChételat, Olivier
dc.contributor.authorRapin, Michaël
dc.contributor.authorBonnal, Benjamin
dc.contributor.authorFivaz, André
dc.contributor.authorWacker, Josias
dc.contributor.authorSporrer, Benjamin
dc.date.accessioned2023-01-20T08:58:55Z
dc.date.available2023-01-20T08:58:55Z
dc.date.issued2022-01
dc.description.abstractBiopotential imaging (e.g., ECGi, EEGi, EMGi) processes multiple potential signals, each requiring an electrode applied to the body’s skin. Conventional approaches based on individual wiring of each electrode are not suitable for wearable systems. Cooperative sensors solve the wiring problem since they consist of active (dry) electrodes connected by a two-wire parallel bus that can be implemented, for example, as a textile spacer with both sides made conductive. As a result, the cumbersome wiring of the classical star arrangement is replaced by a seamless solution. Previous work has shown that potential reference, current return, synchronization, and data transfer functions can all be implemented on a two-wire parallel bus while keeping the noise of the measured biopotentials within the limits specified by medical standards. We present the addition of the power supply function to the two-wire bus. Two approaches are discussed. One of them has been implemented with commercially available components and the other with an ASIC. Initial experimental results show that both approaches are feasible, but the ASIC approach better addresses medical safety concerns and offers other advantages, such as lower power consumption, more sensors on the two-wire bus, and smaller size.
dc.identifier.citationSensors, vol. 22 (21), pp. 8219
dc.identifier.doi10.3390/s22218219
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1131
dc.identifier.urlhttps://www.mdpi.com/1424-8220/22/21/8219
dc.titleRemotely Powered Two-Wire Cooperative Sensors for Biopotential Imaging Wearables
dc.typeJournal Article
dc.type.csemdivisionsBU-D
dc.type.csemresearchareasData & AI
dc.type.csemresearchareasIoT & Vision
dc.type.csemresearchareasDigital Health
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