出汗
可穿戴计算机
汗水
计算机科学
生物医学工程
材料科学
体积热力学
持续监测
实时计算
嵌入式系统
医学
工程类
运营管理
物理
量子力学
内科学
复合材料
作者
Satoko Honda,Ryuki Tanaka,Guren Matsumura,Naruhito Seimiya,Kuniharu Takei
标识
DOI:10.1002/adfm.202306516
摘要
Abstract Flexible sensors that can be attached to the body to collect vital data wirelessly enable real‐time, early‐stage diagnosis for human health management. Wearable sweat sensors have received considerable attention for real‐time physiological monitoring. Unlike conventional methods that require blood‐drawing in a clinic, sweat analyses may enable noninvasive tracking of health conditions for early‐stage diagnosis. Even though a variety of studies to monitor metabolites and other substances have been conducted, automatic, continuous, long‐term, simultaneous monitoring of perspiration rate and electrolytes, which are important parameters in dehydration, has yet to be achieved because of challenges related to sensor design. Here a wireless, wearable, integrated, microfluidic sensor system that can continuously measure these parameters in real‐time for prolonged periods are presented. The proposed sensors are systematically characterized, and machine learning is used to predict device tilt angle to calibrate sensor output signals. Using the sensor design to form a water droplet in a fluidic channel, high‐volume perspiration rate is continuously monitored for more than 7000 s (total sweat volume >170 µL). By testing 10 subjects, physiological responses to ingestion of a sports drink are confirmed by measuring perspiration rhythm changes extracted from real‐time, continuous sweat impedance and rate.
科研通智能强力驱动
Strongly Powered by AbleSci AI