Search

The development of wearable devices for sweat analysis has experienced significant growth in the last two decades, being the main focus the monitoring of athletes health during workouts. One of the main challenges of these approaches has been to attain the continuous monitoring of sweat for time periods over 1 h. This is the main challenge addressed in this work by designing an analytical platform that combines the high performance of potentiometric sensors and a fluidic structure made of a plastic fabric into a multiplexed wearable device. The platform comprises Ion-Sensitive Field-Effect Transistors (ISFETs) manufactured on silicon, a tailor-made solid-state reference electrode, and a temperature sensor integrated into a patch-like polymeric substrate, together with the component that easily collects and drives samples under continuous capillary flow to the sensor areas. ISFET sensors for measuring pH, sodium, and potassium ions were fully characterized in artificial sweat solutions, providing reproducible and stable responses. Then, the real-time and continuous monitoring of the biomarkers in sweat with the wearable platform was assessed by comparing the ISFETs responses recorded during an 85-min continuous exercise session with the concentration values measured using commercial Ion-Selective Electrodes (ISEs) in samples collected at certain times during the session. The developed sensing platform enables the continuous monitoring of biomarkers and facilitates the study of the effects of various real working conditions, such as cycling power and skin temperature, on the target biomarker concentration levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Liquid analysis is key to track conformity with the strict process quality standards of sectors like food, beverage, and chemical manufacturing. In order to analyse product qualities online and at the very point of interest, automated monitoring systems must satisfy strong requirements in terms of miniaturization, energy autonomy, and real time operation. Toward this goal, we present the first implementation of artificial taste running on neuromorphic hardware for continuous edge monitoring applications. We used a solid-state electrochemical microsensor array to acquire multivariate, time-varying chemical measurements, employed temporal filtering to enhance sensor readout dynamics, and deployed a rate-based, deep convolutional spiking neural network to efficiently fuse the electrochemical sensor data. To evaluate performance we created MicroBeTa ( Micro sensor Be verage Ta sting), a new dataset for beverage classification incorporating 7 h of temporal recordings performed over 3 days, including sensor drifts and sensor replacements. Our implementation of artificial taste is 15× more energy efficient on inference tasks than similar convolutional architectures running on other commercial, low power edge-AI inference devices, achieving over 178× lower latencies than the sampling period of the sensor readout, and high accuracy (97%) on a single Intel Loihi neuromorphic research processor included in a USB stick form factor., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 LeBow, Rueckauer, Sun, Rovira, Jiménez-Jorquera, Liu and Margarit-Taulé.)