The aim of this paper is to present an automatic yarn hairiness parameterization method based on optical sensors. Hairiness measurements are performed using a coherent signal processing technique for higher resolution. Using this optical technique together with electronic instrumentation and custom developed software, it is possible to quantify all traditional hairiness parameters (i.e. hairiness (H), its coefficient of variation (CVH) and standard deviation (sH)) used in the textile industry, as well as determine several others, such as mean deviation coefficient (U), deviation rate (DR) and its integral (IDR).The overall goal of the current project is to develop an integrated automatic yarn system characterization: evenness analysis determination using capacitive sensors, hairiness analysis using coherent optics technique and finally, image processing for yarn production characteristics.

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Since microorganisms are evolving rapidly, there is a growing need for a new, fast, and precise technique to analyse blood samples and distinguish healthy from pathological samples. Fourier Transform Infrared (FTIR) spectroscopy can provide information related to the biochemical composition and how it changes when a pathological state arises. FTIR spectroscopy has undergone rapid development over the last decades with a promise of easier, faster, and more impartial diagnoses within the biomedical field. However, thus far only a limited number of studies have addressed the use of FTIR spectroscopy in this field. This paper describes the main concepts related to FTIR and presents the latest research focusing on FTIR spectroscopy technology and its integration in lab-on-a-chip devices and their applications in the biological field. This review presents the potential use of FTIR to distinguish between healthy and pathological samples, with examples of early cancer detection, human immunodeficiency virus (HIV) detection, and routine blood analysis, among others. Finally, the study also reflects on the features of FTIR technology that can be applied in a lab-on-a-chip format and further developed for small healthcare devices that can be used for point-of-care monitoring purposes. To the best of the authors’ knowledge, no other published study has reviewed these topics. Therefore, this analysis and its results will fill this research gap.

There has been an increasing attention to the study of stress. Particularly, college students often experience high levels of stress that are linked to several negative outcomes concerning academic functioning, physical, and mental health. In this paper, we introduce the EuStress Solution, that aims to create an Information System to monitor and assess, continuously and in real-time, the stress levels of the students in order to predict burnout. The Information System will use a measuring instrument based on wearable device and machine learning techniques to collect and process stress-related data from the students without their explicit interaction. In the present study, we focus on heart rate and heart rate variability indices, by comparing baseline and stress condition. We performed different statistical tests in order to develop a complex and intelligent model. Results showed the neural network had the better model fit. KeywordsStress . Heart rate variability metrics . Wearable devices . Medical students This article is part of the Topical Collection on Mobile & Wireless Health

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