The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants’ operational capabilities. Within this framework the ATREC project funded by the “Coincidente” program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems.

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time. Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5 min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test. Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations. Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.

The estimation of body fluids is a useful and common practice for assessment of disease status and therapy outcomes. Electrical bioimpedance spectroscopy (EBIS) methods are noninvasive, inexpensive and efficient alternatives for determination of body fluids. One of the main source of errors in EBIS measurements in the estimation of body fluids is capacitive coupling. In this paper an analysis of capacitive coupling in EBIS measurements was performed and the robustness of the different immittance spectra against it tested. On simulations the conductance (G) spectrum presented the smallest overall error, among all immittance spectra, in the estimation of the impedance parameters used to estimate body fluids. Afterwards the frequency range of 10-500 kHz showed to be the most robust band of the G spectrum. The accuracy of body fluid estimations from the resulting parameters that utilized G spectrum and parameters provided by the measuring device were tested on EBIS clinical measurements from growth hormone replacement therapy patients against estimations performed with dilution methods. Regarding extracellular fluid, the correlation between each EBIS method and dilution was 0.93 with limits of agreement of 1.06 ± 2.95 l for the device, 1.10 ± 2.94 l for G [10-500 kHz] and 1.04 ± 2.94 l for G [5-1000 kHz]. Regarding intracellular fluid, the correlation between dilution and the device was 0.91, same as for G [10-500 kHz] and 0.92 for G [5-1000 kHz]. Limits of agreement were 0.12 ± 4.46 l for the device, 0.09 ± 4.45 for G [10-500 kHz] and 0.04 ± 4.58 for G [5-1000 kHz]. Such close results between the EBIS methods validate the proposed approach of using G spectrum for initial Cole characterization and posterior clinical estimation of body fluids status.

Applications based on measurements of Electrical Bioimpedance (EBI) spectroscopy analysis, like assessment of body composition, have proliferated in the past years. Currently Body Composition Assessment (BCA) based in Bioimpedance Spectroscopy (BIS) analysis relays on an accurate estimation of the Cole parameters R(0) and R(∞). A recent study by Bogonez-Franco et al. has proposed electrode mismatch as source of remarkable artefacts in BIS measurements. Using Total Right Side BIS measurements from the aforementioned study, this work has focused on the influence of electrode mismatch on the estimation of R(0) and R(∞) using the Non-Linear Least Square curve fitting technique on the modulus of the impedance. The results show that electrode mismatch on the voltage sensing electrodes produces an overestimation of the impedance spectrum leading to a wrong estimation of the parameters R(0) and R(∞), and consequently obtaining values around 4% larger that the values obtained from BIS without electrode mismatch. The specific key factors behind electrode mismatch or its influence on the analysis of single and spectroscopy measurements have not been investigated yet, no compensation or correction technique is available to overcome the deviation produced on the EBI measurement. Since textile-enabled EBI applications using dry textrodes, i.e. textile electrodes with dry skin-electrode interfaces and potentially large values of electrode polarization impedance are more prone to produce electrode mismatch, the lack of a correction or compensation technique might hinder the proliferation of textile-enabled EBI applications for personalized healthcare monitoring.

Several applications of Electrical Bioimpedance (EBI) make use of Cole parameters as base of their analysis, therefore Cole parameters estimation has become a very common practice within Multifrequency- and EBI spectroscopy. EBI measurements are very often contaminated with the influence of parasitic capacitances, which contributes to cause a hook-alike measurement artifact at high frequencies in the EBI obtained data. Such measurement artifacts might cause wrong estimations of the Cole parameters, contaminating the whole analysis process and leading to wrong conclusions. In this work, a new approach to estimate the Cole parameters from the real part of the admittance, i.e. the conductance, is presented and its performance is compared with the results produced with the traditional fitting of complex impedance to a depressed semi-circle. The obtained results prove that is feasible to obtain the full Cole equation from only the conductance data and also that the estimation process is safe from the influence capacitive leakage.