Post-Exercise Sweat Loss Estimation Accuracy of Athletes and Physically Active Adults: A Review

Sweat and odor in sportswear - A review

Sportswear worn next to the skin is easily soaked by sweat and may become a breeding ground for the microbiome, thus a source of malodor. Malodor can cause social embarrassment and discomfort to both wearer and others. Given the risks current deodorant products pose to nature and human life, the development of sustainable textiles for odor control comes to the forefront. This review introduces the odor-generating mechanism in clothing from the perspectives of perspiration composition and cutaneous microbiome. With the knowledge of the significant role of sweat in odor formation, the sweat distribution of the human body, measurement techniques, and advanced technologies developed for quick-dry function are presented in the second part. Lastly, odor management in sportswear is evaluated, covering the odor-assessing techniques, the effects of various textile materials, and emerging solutions in terms of antibacterial treatment, adsorbent materials, and photocatalytic degradations of odorous compounds. Overall, it is of both personal and social value to develop novel textile materials with odor-control functions by making use of natural materials and fabric designs.

Predicting hydration status using machine learning models from physiological and sweat biomarkers during endurance exercise: a single case study

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.