Improper hydration routines can reduce athletic performance. Recent studies show that data from noninvasive biomarker recordings can help to evaluate the hydration status of subjects during endurance exercise. These studies are usually carried out on multiple subjects. In this work, we present the first study on predicting hydration status using machine learning models from single-subject experiments, which involve 32 exercise sessions of constant moderate intensity performed with and without fluid intake. During exercise, we measured four noninvasive physiological and sweat biomarkers including heart rate, core temperature, sweat sodium concentration, and whole-body sweat rate. Sweat sodium concentration was measured from six body regions using absorbent patches. We used three machine learning models to determine the percentage of body weight loss as an indicator of dehydration with these biomarkers and compared the prediction accuracy. The results on this single subject show that these models gave similar mean absolute errors, while in general the nonlinear models slightly outperformed the linear model in most of the experiments. The prediction accuracy of using the whole-body sweat rate or heart rate was higher than using core temperature or sweat sodium concentration. In addition, the model trained on the sweat sodium concentration collected from the arms gave slightly better accuracy than from the other five body regions. This exploratory work paves the way for the use of these machine learning models to develop personalized health monitoring together with emerging, noninvasive wearable sensor devices.

Paradoxically more than 50 years after being used in WWII, polycrystalline PbSe technology has turned today into an emerging technology. Without any doubt one of the main facts responsible for the PbSe resurgence is a new method for processing detectors based on a Vapour Phase Deposition (VPD) technique developed at CIDA. Using this method, the first low density 2D PbSe Focal Plane Array (FPA), an x-y addressed type device, was processed on silicon. Even though the last advances have been important they are not yet enough to consider this technology as a real alternative to other uncooled technologies. To reach technical relevance and commercial interest it is obligated to integrate monolithically or hybridize the sensors with their corresponding read out electronics (ROIC). Aiming to process monolithic devices, a proper CMOS read out electronics were designed. In parallel, enabled technologies were developed for adapting the material peculiarities to the CMOS substrates. In this work, the first monolithic device of VPD PbSe is presented. Even though it is a modest 16x16 FPA with a pitch of 200 µm, it represents an important milestone, allocating polycrystalline PbSe among the major players in the short list of uncooled IR detectors. Unlike microbolometers and ferroelectrics, it is a photonic detector suitable for being used as a detector in low cost IR imagers sensitive to the MWIR band and with frame rates as high as 1000 fps. The number of applications is therefore huge, some of them specific, unique and highly demanded in the military and security fields such as sensors applied to fast imagers, Active Protection Systems or low cost seekers.

Mid-wavelength infrared (MWIR) thermography is an emerging technology with promising applications such as industrial monitoring, medicine and automotive, but its use in highspeed cameras is not yet widespread due to the lack of inexpensive sensor integration solutions and their common reliance on bulky cooling mechanisms. This work fills the gap by presenting a monolithic uncooled high-speed imager based on vapor-phase deposition lead selenide (VPD PbSe) photoconductors and a fully digital and configurable CMOS read-out integrated circuit (ROIC) to operate the MWIR imager. This ROIC features cancellation of PbSe dark current, compensation of its output capacitance and correction of the fixed pattern noise (FPN) caused by process non-uniformities in CMOS fabrication and detector deposition. The low-cost 80 × 80 imager has been integrated using 0.35 µm 2P4M standard CMOS technology and PbSe detector post-processing with 135 µm pixel pitch and 68% fill factor values. Experimental optoelectrical performance exhibits 10 bit real-time FPN compensation and DR calibration over the entire focal plane operating at 2 kfps, sub-0.5 LSB inter-pixel crosstalk, sub-µW pixel power consumption, and an overall figure of merit of 55 mK × ms.Index Terms-CMOS, digital pixel sensor (DPS), fixed pattern noise (FPN), high speed, imager, infrared, low cost, low power, MWIR, PbSe, uncooled.

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