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Sweat‐related physiology research has been well established over the years. However, it has only been around ten years that sweat‐based sensing devices started to be explored. With the recent advancements in wearable activity and physiology monitoring devices, sweat was investigated for its contents similar to blood and corresponding wearable devices were studied intensively. This article provides a thorough review on sweating mechanisms, sweat sensing devices, and electronic technologies for sweat sensor implementations. Potential future directions and recommendations based on current research trends were provided in each section. This review aims to offer a unique perspective from both physiology and engineering point‐of‐view to draw a complete landscape of the sweat sensing research.

Dehydration is one of the most profound physiological challenges that significantly affects athletes and soldiers if not detected early. Recently, a few groups have focused on dehydration detection using sweat as the main biomarker. Although there are some proposed devices, the electrical and chemical characteristics of sweat have yet to be incorporated into the validations. In this work, we have developed a simple test setup to analyze artificial sweat that is comprised the main components of human sweat. We provide theoretical and experimental details on the electrical and chemical behavior of the artificial sweat for various concentration values within a temperature range of 5 °C to 50 °C. We have also developed an efficient sweat collecting and detection system based on 3D printing. Human studies were conducted and this particular protocol has shown that dehydration starts to take effect as early as 40 min into the physical activity if there is no fluid intake during the exercise. We believe that our device will lead to developing viable real-time sweat analysis systems.

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This paper proposes and experimentally demonstrates a blind modulation format identification (MFI) method delivering high accuracy (> 99%) even in a low OSNR regime (< 10 dB). By using nonlinear power transformation and peak detection, the proposed MFI can recognize whether the signal modulation format is BPSK, QPSK, 8-PSK or 16-QAM. Experimental results demonstrate that the proposed MFI can achieve a successful identification rate as high as 99% when the incoming signal OSNR is 7 dB. Key parameters, such as FFT length and laser phase noise tolerance of the proposed method, have been characterized.