IEEE Sensors Journal

Healthy sleep is an important indicator of good physical and mental health. The current technology employed for monitoring sleep quality is typically expensive and invasive, which demands innovations in respiration monitoring. In addition, recent effort in developing wearable respiration sensors has faced great challenges in concurrently obtaining low cost, high stretchability, and high sensitivity. Herein, we demonstrate a simple fabrication strategy to construct low-cost, stretchable, skin-breathable and ultrasensitive respiration sensors using pencil drawing, electrode pasting, and laser cutting methods. With smart designs, the sensors exhibited high sensitivity, good stability, decent skin-breathability and capability to detect respiration rate and depth in real-time. The sensors fabricated by the simple low-cost method in this study can be utilized to measure sleep quality, providing warning signals for apnea and sleep disorders. The proposed technology holds promise for developing cost-effective wearable ultrasensitive sensors for sleep quality monitoring.

A gas-sensing array with ten different SnO/sub 2/ sensors was fabricated on a substrate for the purpose of recognizing various kinds and quantities of indoor combustible gas leakages, such as methane, propane, butane, LPG, and carbon monoxide, within their respective threshold limit value (TLV) and lower explosion limit (LEL) range. Nano-sized sensing materials with high surface areas were prepared by coprecipitating SnCl/sub 4/ with Ca and Pt, while the sensing patterns of the SnO/sub 2/-based sensors were differentiated by utilizing different additives. The sensors in the sensor array were designed to produce a uniform thermal distribution along with a high and differentiated sensitivity and reproducibility for low concentrations below 100 ppm. Using the sensing signals of the array, an electronic nose system was then applied to classify and identify simple/mixed explosive gas leakages. A gas pattern recognizer was implemented using a neuro-fuzzy network and multi-layer neural network, including an error-back-propagation learning algorithm. Simulation and experimental results confirmed that the proposed gas recognition system was effective in identifying explosive and hazardous gas leakages. The electronic nose in conjunction with a neuro-fuzzy network was also implemented using a digital signal processor (DSP).

A method for the identification of odors using a dynamically driven sensor array has been developed, in which transient responses of the sensor array were used to recognize target odors. Fourier transformation was used to transform the transient response curves of the sensor array into Fourier spectra, and patterns composed of some of the magnitudes in the spectra were used in the pattern recognition to identify the odors. Identification of odors was performed using this method with three kinds of 10% ethanol solution of tea extract and three kinds of Japanese soy sauce. In consequence, the sample odors could be correctly recognized without any pretreatment device for separation of minor components from the main component.

The performance of polymer carbon-black composite chemical vapor sensors as a function of underlying electrode size and geometry has been studied. The sensor performance parameters investigated were sensor response magnitude to a toluene analyte (100, 500, and 1000 ppm), fundamental sensor noise in the presence of air, and two concentrations of toluene (100 and 500 ppm), and signal-to-noise ratio (100 and 500 ppm). An array of sensors with 42 different circular electrode configurations were designed, fabricated, and tested where electrode gap was varied from 10 to 500 /spl mu/m and the diameter of the sensors was varied from 30 to 2000 /spl mu/m. Each array of electrodes was coated with an approximately 1 /spl mu/m-thick layer of conducting polymer carbon-black composite with an insulating poly(alkylacrylate) polymer. The response magnitude, fundamental noise, and signal-to-noise ratio of each sensor was measured and compared to electrode geometry, such as electrode gap, aspect ratio, and overall size. No significant dependence of sensor response magnitude and noise to electrode configuration has been observed to be larger than the variation from sensor to sensor. However, the signal-to-noise ratio tended to decrease for sensors with the smallest scales.

Results for optimizing an array of conducting polymer gas sensors for sensing one of five analytes in the presence of up to four interferents are presented. The optimized array consists of subarrays of homogeneous (like) sensors contributing to a larger heterogeneous array of up to ten points (unlike sensors) in multidimensional sensor space. The optimization techniques presented here are linear, since the polymer sensors in their useful (low concentration) operating range exhibit linear and additive response characteristics. The optimization of these arrays produces maximum separability between analytes, demonstrating the trade-off between the addition of both information and variability induced by increasing the size of the heterogeneous array. Optimization results for sensing acetone, hexane, THF, toluene, and ethanol in the presence of interferents result in array sizes that are significantly less than the maximum available number of sensors (ten in the heterogeneous partition of the array). This result adds fuel to the argument that fewer sensors are better; the argument for more sensors, however, is also made in the context of the electronic nose systems where significant chemical diversity is required. Homogeneous subarrays of up to four elements each improve the separability of analytes in these optimized heterogeneous arrays by over 10% and also effectively flag broken or unhealthy sensors in a manner that is independent of analyte and concentration.

The Cyranose 320 (Cyrano Sciences Inc., USA), comprising an array of 32 polymer carbon black composite sensors, has been used to identify species of bacteria commonly associated with medical conditions. Results from two experiments are presented: one on bacteria causing eye infections and one on a new series of tests on bacteria responsible for some ear, nose, and throat (ENT) diseases. For the eye bacteria tests, pure lab cultures were used and the electronic nose (EN) was used to sample the headspace of sterile glass vials containing a fixed volume of bacteria in suspension. For the ENT bacteria, the system was taken a step closer toward medical application, as readings were taken from the headspace of the same blood agar plates used to culture real samples collected from patients. After preprocessing, principal component analysis (PCA) was used as an exploratory technique to investigate the clustering of vectors in multi-sensor space. Artificial neural networks (ANNs) were then used as predictors, and a multilayer perceptron (MLP) trained with back-propagation (BP) and with Levenberg-Marquardt was used to identify the different bacteria. The optimal MLP was found to correctly classify 97.3% of the six eye bacteria of interest and 97.6% of the four ENT bacteria including two sub-species. A radial basis function (RBF) network was able to discriminate between the six eye bacteria species, even in the lowest state of concentration, with 92.8% accuracy. These results show the potential application of the Cyranose together with neural network-based predictors, for rapid screening and early detection of bacteria associated with these medical conditions, and the possible development of this EN system as a near-patient tool in primary medical healthcare.

This paper describes a portable electronic nose based on embedded PC technology. The instrument combines a small footprint with the versatility offered by embedded technology in terms of software development and digital communications services. A summary of the proposed hardware and software solutions is provided with an emphasis on data processing. Data evaluation procedures available in the instrument include automatic feature selection by means of SFFS, feature extraction with linear discriminant analysis (LDA) and principal component analysis (PCA), multi-component analysis with partial least squares (PLS) and classification through k-NN and Gaussian mixture models. In terms of instrumentation, the instrument makes use of temperature modulation to improve the selectivity of commercial metal oxide gas sensors. Field applications of the instrument, including experimental results, are also presented.

Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute for the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics have been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction.

Linear black-box modeling techniques are applied to both steady state and dynamic data gathered from two electronic nose experiments involving cyanobacteria cultures. Analysis of the data from a strain identification experiment shows that very simple low order MISO black box model structures are able to produce very high success rates (up to 100%) when identifying the toxic strain of cyanobacteria. This is comparable with the best success rates for steady state data reported elsewhere using artificial neural networks. Analysis of data from a growth phase identification experiment using MIMO black-box models produces success rates of 82.3% for steady state data and 76.6% for dynamic data. This compares poorly with the best performing nonlinear artificial neural networks, which obtained a 95.1% success rate on the same data. This demonstrates the limitations of these linear techniques when applied to more difficult problems.

Results from systematic gas sensing experiments on polymer coated surface-transverse-wave (STW) and surface-acoustic-wave (SAW) based two-port resonators on rotated Y-cut quartz, operating at the same acoustic wavelength of 7.22 /spl mu/m, are presented. The acoustic devices are coated with chemosensitive films of different viscoelastic properties and thicknesses, such as solid hexamethyldisiloxane (HMDSO), semisolid styrene (ST), and soft allyl alcohol (AA). The sensor sensitivities to vapors of different chemical analytes are automatically measured in a sensor head, evaluated, and compared. It is shown that thin HMDSO- and ST-coated STW sensors are up to 3.8 times more sensitive than their SAW counterparts, while SAW devices coated with thick soft AA-films are up to 3.6 times more sensitive than the STW ones. This implies that SAWs are more suitable for operation with soft coatings while STWs perform better with solid and semisolid films. A close-to-carrier phase noise evaluation shows that the vapor flow homogeneity, the analyte concentration, its sorption dynamics, and the sensor oscillator design are the major limiting factors for the sensor noise and its resolution. A well designed ST-coated 700 MHz STW sensor provides a 178 kHz sensor signal at a 630 ppm concentration of tetra-chloroethylene and demonstrates short-term stability of 3/spl times/10/sup -9//s which results in a sensor resolution of about 7 parts per billion (ppb).