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An epifluidic electronic patch with spiking sweat clearance for event-driven perspiration monitoring.

ABSTRACT: Sensory neurons generate spike patterns upon receiving external stimuli and encode key information to the spike patterns, enabling energy-efficient external information processing. Herein, we report an epifluidic electronic patch with spiking sweat clearance using a sensor containing a vertical sweat-collecting channel for event-driven, energy-efficient, long-term wireless monitoring of epidermal perspiration dynamics. Our sweat sensor contains nanomesh electrodes on its inner wall of the channel and unique sweat-clearing structures. During perspiration, repeated filling and abrupt emptying of the vertical sweat-collecting channel generate electrical spike patterns with the sweat rate and ionic conductivity proportional to the spike frequency and amplitude over a wide dynamic range and long time (> 8 h). With such 'spiking' sweat clearance and corresponding electronic spike patterns, the epifluidic wireless patch successfully decodes epidermal perspiration dynamics in an event-driven manner at different skin locations during exercise, consuming less than 0.6% of the energy required for continuous data transmission. Our patch could integrate various on-skin sensors and emerging edge computing technologies for energy-efficient, intelligent digital healthcare.

SUBMITTER: Kim S

PROVIDER: S-EPMC9640696 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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An epifluidic electronic patch with spiking sweat clearance for event-driven perspiration monitoring.

Kim Sangha S Park Seongjin S Choi Jina J Hwang Wonseop W Kim Sunho S Choi In-Suk IS Yi Hyunjung H Kwak Rhokyun R

Nature communications 20221107 1

Sensory neurons generate spike patterns upon receiving external stimuli and encode key information to the spike patterns, enabling energy-efficient external information processing. Herein, we report an epifluidic electronic patch with spiking sweat clearance using a sensor containing a vertical sweat-collecting channel for event-driven, energy-efficient, long-term wireless monitoring of epidermal perspiration dynamics. Our sweat sensor contains nanomesh electrodes on its inner wall of the channe ...[more]

PMID: 36344563

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Project description:Tactile sensing is essential for a variety of daily tasks. Inspired by the event-driven nature and sparse spiking communication of the biological systems, recent advances in event-driven tactile sensors and Spiking Neural Networks (SNNs) spur the research in related fields. However, SNN-enabled event-driven tactile learning is still in its infancy due to the limited representation abilities of existing spiking neurons and high spatio-temporal complexity in the event-driven tactile data. In this paper, to improve the representation capability of existing spiking neurons, we propose a novel neuron model called "location spiking neuron," which enables us to extract features of event-based data in a novel way. Specifically, based on the classical Time Spike Response Model (TSRM), we develop the Location Spike Response Model (LSRM). In addition, based on the most commonly-used Time Leaky Integrate-and-Fire (TLIF) model, we develop the Location Leaky Integrate-and-Fire (LLIF) model. Moreover, to demonstrate the representation effectiveness of our proposed neurons and capture the complex spatio-temporal dependencies in the event-driven tactile data, we exploit the location spiking neurons to propose two hybrid models for event-driven tactile learning. Specifically, the first hybrid model combines a fully-connected SNN with TSRM neurons and a fully-connected SNN with LSRM neurons. And the second hybrid model fuses the spatial spiking graph neural network with TLIF neurons and the temporal spiking graph neural network with LLIF neurons. Extensive experiments demonstrate the significant improvements of our models over the state-of-the-art methods on event-driven tactile learning, including event-driven tactile object recognition and event-driven slip detection. Moreover, compared to the counterpart artificial neural networks (ANNs), our SNN models are 10× to 100× energy-efficient, which shows the superior energy efficiency of our models and may bring new opportunities to the spike-based learning community and neuromorphic engineering. Finally, we thoroughly examine the advantages and limitations of various spiking neurons and discuss the broad applicability and potential impact of this work on other spike-based learning applications.

Dataset Information

An epifluidic electronic patch with spiking sweat clearance for event-driven perspiration monitoring.

Publications

An epifluidic electronic patch with spiking sweat clearance for event-driven perspiration monitoring.

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