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.

Very often in Electrical Bioimpedance (EBI) spectroscopy measurements the presence of stray capacitances creates a measurement artefact commonly known as Hook Effect . Such an artefact creates a hook-alike deviation of the EBI data noticeable when representing the measurement on the impedance plane. Such Hook Effect is noticeable at high frequencies but it also causes a data deviation at lower measurement frequencies. In order to perform any accurate analysis of the EBI spectroscopy data, the influence of the Hook Effect must be removed. An established method to compensate the hook effect is the well known Td compensation , which consists on multiplying the obtained spectrum, Z meas (ω) by a complex exponential in the form of exp[jωTd]. Such a method cannot correct entirely the Hook Effect since the hook-alike deviation occurs a broad frequency range in both magnitude and phase of the measured impedance, and by using a scalar value for Td . First a scalar only modifies the phase of the measured impedance and second, a single value can truly corrects the Hook Effect only at a single frequency. In addition, the process to select a value for the scalar Td by an iterative process with the aim to obtain the best Cole fitting lacks solid scientific grounds. In this work the Td compensation method is revisited and a modified approach for correcting the Hook Effect including a novel method for selecting the correcting values is proposed. The initial validation results confirm that the proposed method entirely corrects the Hook Effect at all frequencies.

QC 20120206

Determining the stress level of a subject in real time could be of special interest in certain professional activities to allow the monitoring of soldiers, pilots, emergency personnel and other professionals responsible for human lives. Assessment of current mental fitness for executing a task at hand might avoid unnecessary risks. To obtain this knowledge, two physiological measurements were recorded in this work using customized non-invasive wearable instrumentation that measures electrocardiogram (ECG) and thoracic electrical bioimpedance (TEB) signals. The relevant information from each measurement is extracted via evaluation of a reduced set of selected features. These features are primarily obtained from filtered and processed versions of the raw time measurements with calculations of certain statistical and descriptive parameters. Selection of the reduced set of features was performed using genetic algorithms, thus constraining the computational cost of the real-time implementation. Different classification approaches have been studied, but neural networks were chosen for this investigation because they represent a good tradeoff between the intelligence of the solution and computational complexity. Three different application scenarios were considered. In the first scenario, the proposed system is capable of distinguishing among different types of activity with a 21.2% probability error, for activities coded as neutral, emotional, mental and physical. In the second scenario, the proposed solution distinguishes among the three different emotional states of neutral, sadness and disgust, with a probability error of 4.8%. In the third scenario, the system is able to distinguish between low mental load and mental overload with a probability error of 32.3%. The computational cost was calculated, and the solution was implemented in commercially available Android-based smartphones. The results indicate that execution of such a monitoring solution is negligible compared to the nominal computational load of current smartphones.

Often when performing electrical bioimpedance (EBI) spectroscopy measurements, the obtained EBI data present a hook-like deviation, which is most noticeable at high frequencies in the impedance plane. The deviation is due to a capacitive leakage effect caused by the presence of stray capacitances. In addition to the data deviation being remarkably noticeable at high frequencies in the phase and the reactance spectra, the measured EBI is also altered in the resistance and the modulus. If this EBI data deviation is not properly removed, it interferes with subsequent data analysis processes, especially with Cole model-based analyses. In other words, to perform any accurate analysis of the EBI spectroscopy data, the hook deviation must be properly removed. Td compensation is a method used to compensate the hook deviation present in EBI data; it consists of multiplying the obtained spectrum, Z meas (ω), by a complex exponential in the form of exp(-jωTd). Although the method is well known and accepted, Td compensation cannot entirely correct the hook-like deviation; moreover, it lacks solid scientific grounds. In this work, the Td compensation method is revisited, and it is shown that it should not be used to correct the effect of a capacitive leakage; furthermore, a more developed approach for correcting the hook deviation caused by the capacitive leakage is proposed. The method includes a novel correcting expression and a process for selecting the proper values of expressions that are complex and frequency dependent. The correctness of the novel method is validated with the experimental data obtained from measurements from three different EBI applications. The obtained results confirm the sufficiency and feasibility of the correcting method.

Since there are several applications of Electrical Bioimpedance (EBI) that use the Cole parameters as base of the analysis, to fit EBI measured data onto the Cole equation is a very common practice within Multifrequency-EBI and spectroscopy. The aim of this paper is to compare different fitting methods for EBI data in order to evaluate their suitability to fit the Cole equation and estimate the Cole parameters. Three of the studied fittings are based on the use of Non-Linear Least Squares on the Cole model, one using the real part only, a second using the imaginary part and the third using the complex impedance. Furthermore, a novel fitting method done on the Impedance plane, without using any frequency information has been implemented and included in the comparison. Results show that the four methods perform relatively well but the best fitting in terms of Standard Error of Estimate is the fitting obtained from the resistance only. The results support the possibility of measuring only the resistive part of the bioimpedance to accurately fit Cole equation and estimate the Cole parameters, with entailed advantages.