F. Di Francesco

BIOTEX—Biosensing Textiles for Personalised Healthcare Management

Textile-based sensors offer an unobtrusive method of continually monitoring physiological parameters during daily activities. Chemical analysis of body fluids, noninvasively, is a novel and exciting area of personalized wearable healthcare systems. BIOTEX was an EU-funded project that aimed to develop textile sensors to measure physiological parameters and the chemical composition of body fluids, with a particular interest in sweat. A wearable sensing system has been developed that integrates a textile-based fluid handling system for sample collection and transport with a number of sensors including sodium, conductivity, and pH sensors. Sensors for sweat rate, ECG, respiration, and blood oxygenation were also developed. For the first time, it has been possible to monitor a number of physiological parameters together with sweat composition in real time. This has been carried out via a network of wearable sensors distributed around the body of a subject user. This has huge implications for the field of sports and human performance and opens a whole new field of research in the clinical setting.

A Wearable Sensor for Measuring Sweat Rate

Wearable sensors present a new frontier in the development of monitoring techniques. They are of great importance in sectors such as sport and healthcare as they enable physiological signals and biological fluids, such as human sweat, to be continuously monitored. Until recently this could only be carried out in specialized laboratories using cumbersome and often expensive devices. Sweat monitoring sensors integrated onto textile substrates are not only innovative but they also represent the first attempt to use such an idea in a system that will be worn directly on the body. This study outlines the development of a wearable sweat-rate sensor integrated onto a textile.

Monitoring breath during oral glucose tolerance tests.

The evolution of breath composition during oral glucose tolerance tests (OGTTs) was analysed by thermal desorption/gas chromatography/mass spectrometry in 16 subjects and correlated to blood glucose levels. The glucose tolerance tests classified five of the subjects as diabetics, eight as affected by impaired glucose tolerance and three as normoglycaemic. Acetone levels were generally higher in diabetics (average concentration values: diabetics, 300 ± 40 ppbv; impaired glucose tolerance, 350 ± 30 ppbv; normoglycaemic, 230 ± 20 ppbv) but the large inter-individual variability did not allow us to identify the three groups by this parameter alone. The exhalation of 3-hydroxy-butan-2-one and butane-2,3-dione, likely due to the metabolization of glucose by bacteria in the mouth, was also observed. Future work will involve the extension of the analyses to other volatile compounds by attempting to improve the level of discrimination between the various classes of subjects.

Implementation of Fowler's method for end-tidal air sampling.

The design, realization and testing of a CO(2)-triggered breath sampler, capable of a separate collection of dead space and end-tidal air on multiple breaths, is presented. This sampling procedure has advantages in terms of the sample volume, insights regarding the origin of compounds, increased reproducibility and higher concentrations of compounds. The high quality of design and the speed of the components ensure a breath-by-breath estimate of dead volume, as well as the comfort and safety of the subject under test. The system represents a valid tool to contribute to the development of a standardized sampling protocol needed to compare results obtained by the various groups in this field.

Assessment of Bioinspired Models for Pattern Recognition in Biomimetic Systems

The increasing complexity of the artificial implementations of biological systems, such as the so-called electronic noses (e-noses) and tongues (e-tongues), poses issues in sensory feature extraction and fusion, drift compensation and pattern recognition, especially when high reliability is required. In particular, in order to achieve effective results, the pattern recognition system must be carefully designed. In order to investigate a novel biomimetic approach for the pattern recognition module of such systems, the classification capabilities of an artificial model inspired by the mammalian cortex, a cortical-based artificial neural network (CANN), are compared with several artificial neural networks present in the e-nose and e-tongue literature, a multilayer perceptron (MLP), a Kohonen self-organizing map (KSOM) and a fuzzy Kohonen self-organizing map (FKSOM). Each network was tested with large datasets coming from a conducting polymer-sensor-based e-nose and a composite array-based e-tongue. The comparison of results showed that the CANN model is able to strongly enhance the performances of both systems.

Comparison of sampling bags for the analysis of volatile organic compounds in breath.

Nalophan, Tedlar and Cali-5-Bond polymeric bags were compared to determine the most suitable type for breath sampling and storage when volatile organic compounds are to be determined. Analyses were performed by thermal desorption gas chromatography mass spectrometry. For each bag, the release of contaminants and the chemical stability of a gaseous standard mixture containing eighteen organic compounds, as well as the CO2 partial pressure were assessed. The selected compounds were representative of breath constituents and belonged to different chemical classes (i.e. hydrocarbons, ketones, aldehydes, aromatics, sulfurs and esters). In the case of Nalophan, the influence of the surface-to-volume ratio, related to the bags filling degree, on the chemical stability was also evaluated. Nalophan bags were found to be the most suitable in terms of contaminants released during storage (only 2-methyl-1,3-dioxalane), good sample stability (up to 24 h for both dry and humid samples), and very limited costs (about 1 € for a 20 liter bag). The (film) surface-to-(sample) volume ratio was found to be an important factor affecting the stability of selected compounds, and therefore we recommended to fill the bag completely.

Skin temperature monitoring by a wireless sensor

The present paper reports the development of a temperature data logger based on a temperature sensitive resistive film and an RFID tag. Thanks to its capability of wireless communication, the device is going to be used for a minimally invasive remote monitoring of the skin temperature under a bandage or a wound dressing.

Towards a Real-Time Transduction and Classification of Chemoresistive Sensor Array Signals

Recently, a growing interest in artificial implementations of biological systems has been arising. In particular, several research groups have been working in mimicking the mammalian olfactory system with the so-called electronic noses (e-noses). The e-noses, which are based on a sensor array, a fluid-dynamic system, and a data processing unit, are systems devoted to detecting and analyzing volatiles, where a deep knowledge of the target application is needed. In order to achieve effective results the sampling system, the measurement protocols, the sensor array, and the pattern recognition techniques have to be carefully designed. The increasing complexity of such design poses issues in sensory feature extraction and fusion, drift compensation, and data processing, especially when high efficiency is required for real-time applications. The interconnection and cooperation of several modules devoted to processing different tasks, such as control, data acquisition, data filtering interfaces, feature selection, and pattern analysis, are already mandatory. Moreover, heterogeneous techniques used to implement such tasks may introduce module interconnection and cooperation issues. In this paper, we address the development of a dedicated instrument able to perform real-time transduction, fusion, and processing of chemoresistive sensor array signals. In particular, this instrument realizes a dynamic and efficient management of data processing techniques and automatically controls the measurement protocols and the sampling system. An array of conducting poly (alkoxy-bithiophenes) sensors, the fluid-dynamic system, the electronic section, the frameworks base architecture, and the implementation of dedicated application processes are described. The classification task is based on a self-organizing map where models for artificial neurons and connections were derived from the base structures available in the framework core. According to the target application, this instrument is portable and easily tailored, calibrated, and trained. Classification of olive oil headspaces supports its utility in supplying high-efficiency routine for volatile organic compounds detection and analysis

A neural approach for improving the measurement capability of an electronic nose

Electronic noses, instruments for automatic recognition of odours, are typically composed of an array of partially selective sensors, a sampling system, a data acquisition device and a data processing system. For the purpose of evaluating the quality of olive oil, an electronic nose based on an array of conducting polymer sensors capable of discriminating olive oil aromas was developed. The selection of suitable pattern recognition techniques for a particular application can enhance the performance of electronic noses. Therefore, an advanced neural recognition algorithm for improving the measurement capability of the device was designed and implemented. This method combines multivariate statistical analysis and a hierarchical neural-network architecture based on self-organizing maps and error back-propagation. The complete system was tested using samples composed of characteristic olive oil aromatic components in refined olive oil. The results obtained have shown that this approach is effective in grouping aromas into different categories representative of their chemical structure.

A dual mode breath sampler for the collection of the end-tidal and dead space fractions

This work presents a breath sampler prototype automatically collecting end-tidal (single and multiple breaths) or dead space air fractions (multiple breaths). This result is achieved by real time measurements of the CO2 partial pressure and airflow during the expiratory and inspiratory phases. Suitable algorithms, used to control a solenoid valve, guarantee that a Nalophan(®) bag is filled with the selected breath fraction even if the subject under test hyperventilates. The breath sampler has low pressure drop (<0.5 kPa) and uses inert or disposable components to avoid bacteriological risk for the patients and contamination of the breath samples. A fully customisable software interface allows a real time control of the hardware and software status. The performances of the breath sampler were evaluated by comparing (a) the CO2 partial pressure calculated during the sampling with the CO2 pressure measured off-line within the Nalophan(®) bag; (b) the concentrations of four selected volatile organic compounds in dead space, end-tidal and mixed breath fractions. Results showed negligible deviations between calculated and off-line CO2 pressure values and the distributions of the selected compounds into dead space, end-tidal and mixed breath fractions were in agreement with their chemical-physical properties.