School of Engineering and Technology, Central Michigan University, Mt Pleasant, MI 48859, USA.
Department of Physical Therapy and Human Movement Science, Sacred Heart University, Fairfield, CT 06825, USA.
Sensors (Basel). 2018 Feb 10;18(2):533. doi: 10.3390/s18020533.
There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant information on the physiological condition of the human body. However, it is important to know the sweat rate as well, as sweat rate alters the concentration of the sweat constituents, and ultimately affects the accuracy of hydration detection. Towards this goal, a calorimetric based flow-rate detection system was built and tested to determine sweat rate in real time. The proposed sweat rate monitoring system has been validated through both controlled lab experiments (syringe pump) and human trials. An Internet of Things (IoT) platform was embedded, with the sensor using a Simblee board and Raspberry Pi. The overall prototype is capable of sending sweat rate information in real time to either a smartphone or directly to the cloud. Based on a proven theoretical concept, our overall system implementation features a pioneer device that can truly measure the rate of sweat in real time, which was tested and validated on human subjects. Our realization of the real-time sweat rate watch is capable of detecting sweat rates as low as 0.15 µL/min/cm², with an average error in accuracy of 18% compared to manual sweat rate readings.
在过去的十年中，人们对汗水的生理学进行了大量研究，其中一个主要关注点是开发一种利用汗水的实时水合监测器。几十年来，人们已经了解了汗水的成分；汗水为人体的生理状况提供了重要信息。然而，了解出汗率也很重要，因为出汗率会改变汗液成分的浓度，最终会影响水合检测的准确性。为此，我们构建并测试了一种基于量热法的流速检测系统，以实时确定出汗率。通过控制实验室实验（注射器泵）和人体试验对提出的出汗率监测系统进行了验证。物联网 (IoT) 平台被嵌入其中，传感器使用 Simblee 板和 Raspberry Pi。整体原型能够实时将出汗率信息发送到智能手机或直接发送到云端。基于经过验证的理论概念，我们的整体系统实现具有开创性的设备，可以真正实时测量出汗率，并在人体上进行了测试和验证。我们的实时出汗率手表能够检测低至 0.15 µL/min/cm² 的出汗率，与手动出汗率读数相比，平均误差为 18%。
