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.

Fig. 1. Schematic illustration of spike encoding of external stimuli of a biological sensory neuron and a sweat VIA sensor.

a A biological sensory neuron that generates spike patterns upon receiving external stimuli through receptors with the spike frequency proportional to the stimuli intensity. b A sweat VIA sensor that generates spike patterns upon exposure to perspiration with the spike frequency proportional to the sweat rate, which enables event-driven, energy-efficient perspiration monitoring.

Fig. 2. Structures and operating principles of the sweat VIA sensor and event-driven wireless monitoring of epidermal perspiration.

Schematic diagrams of a the structures and operating principles of the sweat VIA sensor and b its equivalent circuit. c Example of a spike admittance curve produced by the sweat VIA sensor during the collection and clearance of sweat. d Schematic of the sweat rate versus spike frequency and sweat conductivity versus peak admittance of the spike signal. e The sweat VIA-based epifluidic wireless patch that wirelessly transmits measured data to a mobile phone via BLE. f Illustration of spiking-event-driven data transmission containing key information about sweat rate and ionic conductivity. The spike frequency and amplitude encode the sweat rate and ionic conductivity, respectively, with the spiking-event-driven readout requiring many fewer wireless data transmissions than a continuous readout.

Fig. 3. Characteristics of the sweat VIA sensor.

a Photograph of the VIA channel and nanomesh electrode. Scanning electron microscope images of b the nanomesh electrode and c CNT-PDMS sponge. d Side-view optical image of the assembled sweat VIA sensor. e Experimental setup for visualizing sweat flow in the sweat VIA sensor and measuring admittance. f Fluorescence micrographs showing clearance of the sweat solution as it contacted the CNT-PDMS sponge. g Measured admittance curve from the sweat VIA sensor during the continuous flow of a 92.7 mM NaCl solution (10,350 μS/cm) at a flow rate of 0.5 μL/min. Solution clearance occurred at each point at which the admittance suddenly decreased (in the red box for one case).

Fig. 4. Encoding the flow rate and ionic conductivity into admittance spikes using the sweat VIA sensor.

Continuous monitoring of the electrical admittance when the simulated sweat (92.7 mM NaCl solution, which has a conductivity of 10,350 μS/cm) experiences a change of flow rate a from 0.1 to 0.5 μL/min and b from 1 to 10 μL/min. The average spiking signals of the admittance curve at each flow rate of c 0.1–0.5 μL/min and d 1–10 μL/min. e. Dependence of spike frequency on the flow rate calculated from the admittance curves in a and b. The data points for flow rates of 0.1–3 μL/min were well fit by a straight line (red line), but all the data points were better fit by a nonlinear curve considering a non-negligible sweat clearance time (blue line). The detailed derivation of the fitting equations is given in the Supplementary Information. f Continuous monitoring of the admittance of the sweat VIA sensor with aqueous solutions of NaCl at concentrations of 2.4, 12.8, 48.8, and 92.7 mM (273; 1,737; 4,945; and 10,350 μS/cm), each at a flow rate of 0.5 μL/min, and the resulting encoding of ionic concentration into the admittance peak amplitude.

Fig. 5. A sweat VIA-based epifluidic wireless patch.

a Photograph of the flexible printed circuit board–based wireless sensor platform for monitoring epidermal perspiration dynamics using the sweat VIA sensor. b Schematic diagram of the sweat VIA-based wireless sensor platform. c System-level block algorithm for the sweat VIA sensor enabling spiking-event-driven wireless data transmission. d Comparison of the energy consumption of wireless data transmission in a continuous manner with that in a spiking-event-driven manner. e Exploded-view illustration of the sweat VIA-based epifluidic wireless patch. f Photograph of the assembled sweat VIA-based epifluidic patch. For clarity, the CNT-PDMS sponge and battery are not shown here.

Fig. 6. On-body study of event-driven wireless monitoring of epidermal perspiration.

a Schematic and photographic images of the on-body study using the sweat VIA-based epifluidic wireless patches (C, R) and absorbent cotton pads (L) attached to the indicated chest skin areas. C: center, R: right, L: left area of the chest skin. b Admittance values of the sweat VIA sensor calculated using wireless readouts transmitted in continuous (black line) and spiking-event-driven (red inverted triangle) manners during the entire exercise sequence of 100 min with various cycling load levels, with the higher number indicating a harder exercise load. c Enlarged graphs of the continuous admittance data shown in b obtained under different exercise load levels. d Sweat rates and e conductivity levels were obtained using the spiking-event-driven wireless readouts from each epifluidic patch and the laboratory analysis of the sweat collected manually using absorbent cotton pads during the exercise sequence shown in a. f Measured volumes of cumulative sweat loss through the sweat VIA-based epifluidic patch and absorbent pads. In panels, b–f, the dashed green lines mark when the cycling load level changed.