Thermoregulation and thermography in neonatal physiology and disease.

INTRODUCTION: Infrared thermal imaging, or thermography, is a technique used to measure body surface temperature in the study of thermoregulation. Researchers are beginning to use this novel methodology to study cancer, peripheral vascular disease, and wound management. METHODS: The authors tested the feasibility of using an FLIR SC640 uncooled, infrared camera to measure body temperature in neonates housed in heated, humid incubators. The authors examined thermograms to analyze distributions between central and peripheral body temperature in extremely low birth weight infants. The authors have also used this technology to examine the relationship between body temperature and development of necrotizing enterocolitis in premature infants. RESULTS: Handheld, uncooled, infrared cameras are easy to use and produce high-quality thermograms that can be visualized in grayscale or color palettes to enhance qualitative and quantitative analyses. CONCLUSION: Future research will benefit from the use of this noninvasive, inexpensive measurement tool. Nurse researchers can use this methodology in adult and infant populations to study temperature differentials present in pathological conditions.

Technical note: rapid prototyping of 3D grid arrays for image guided therapy quality assurance.

Three dimensional grid phantoms offer a number of advantages for measuring imaging related spatial inaccuracies for image guided surgery and radiotherapy. The authors examined the use of rapid prototyping technology for directly fabricating 3D grid phantoms from CAD drawings. We tested three different fabrication process materials, photopolymer jet with acrylic resin (PJ/AR), selective laser sintering with polyamide (SLS/P), and fused deposition modeling with acrylonitrile butadiene styrene (FDM/ABS). The test objects consisted of rectangular arrays of control points formed by the intersections of posts and struts (2 mm rectangular cross section) and spaced 8 mm apart in the x, y, and z directions. The PJ/AR phantom expanded after immersion in water which resulted in permanent warping of the structure. The surface of the FDM/ABS grid exhibited a regular pattern of depressions and ridges from the extrusion process. SLS/P showed the best combination of build accuracy, surface finish, and stability. Based on these findings, a grid phantom for assessing machine-dependent and frame-induced MR spatial distortions was fabricated to be used for quality assurance in stereotactic neurosurgical and radiotherapy procedures. The spatial uniformity of the SLS/P grid control point array was determined by CT imaging (0.6 x 0.6 x 0.625 mm3 resolution) and found suitable for the application, with over 97.5% of the control points located within 0.3 mm of the position specified in CAD drawing and none of the points off by more than 0.4 mm. Rapid prototyping is a flexible and cost effective alternative for development of customized grid phantoms for medical physics quality assurance.

A pyroelectric infrared biometric system for real-time walker recognition by use of a maximum likelihood principal components estimation (MLPCE) method.

This paper presents a novel biometric system for real-time walker recognition using a pyroelectric infrared sensor, a Fresnel lens array and signal processing based on the linear regression of sensor signal spectra. In the model training stage, the maximum likelihood principal components estimation (MLPCE) method is utilized to obtain the regression vector for each registered human subject. Receiver operating characteristic (ROC) curves are also investigated to select a suitable threshold for maximizing subject recognition rate. The experimental results demonstrate the effectiveness of the proposed pyroelectric sensor system in recognizing registered subjects and rejecting unknown subjects.

Lightweight biometric detection system for human classification using pyroelectric infrared detectors.

We use pyroelectric detectors that are differential in nature to detect motion in humans by their heat emissions. Coded Fresnel lens arrays create boundaries that help to localize humans in space as well as to classify the nature of their motion. We design and implement a low-cost biometric tracking system by using off-the-shelf components. We demonstrate two classification methods by using data gathered from sensor clusters of dual-element pyroelectric detectors with coded Fresnel lens arrays. We propose two algorithms for person identification, a more generalized spectral clustering method and a more rigorous example that uses principal component regression to perform a blind classification.

Path-dependent human identification using a pyroelectric infrared sensor and fresnel lens arrays.

This paper presents a design and development of a low power consumption, and low cost, human identification system using a pyroelectric infrared (PIR) sensor whose visibility is modulated by a Fresnel lens array. The optimal element number of the lens array for the identification system was investigated and the experimental results suggest that the lens array with more elements can yield a better performance in terms of identification and false alarm rates. The other parameters of the system configuration such as the height of sensor location and sensor-to-object distance were also studied to improve spectral distinctions among sensory data of human objects. The identification process consists of two parts: training and testing. For the data training, we employed a principal components regression (PCR) method to cluster data with respect to different registered objects at different speed levels. The feature data of different objects walking along the same path in training yet at random speeds are then tested against the pre-trained clusters to decide whether the target is registered, and which member of the registered group it is.

Real-time human identification using a pyroelectric infrared detector array and hidden Markov models.

This paper proposes a real-time human identification system using a pyroelectric infrared (PIR) detector array and hidden Markov models (HMMs). A PIR detector array with masked Fresnel lens arrays is used to generate digital sequential data that can represent a human motion feature. HMMs are trained to statistically model the motion features of individuals through an expectation-maximization (EM) learning process. Human subjects are recognized by evaluating a set of new feature data against the trained HMMs using the maximum-likelihood (ML) criterion. We have developed a prototype system to verify the proposed method. Sensor modules with different numbers of detectors and different sampling masks were tested to maximize the identification capability of the sensor system.

Fiber-optic localization by geometric space coding with a two-dimensional gray code.

With the objective of monitoring motion within a room, we segment the two-dimensional (2D) floor space into discrete cells and encode each cell with a binary code word generated by a fiber. We design a set of k-neighbor-local codes to localize an extended object and, particularly when k = 2, employ a 2D gray code to localize a human by tracking his or her footsteps. Methods for implementing the codes in a fiber web are discussed, and we demonstrate the experimental result with the fiber mat. The observed system performance confirms the theoretical analysis. The space coding technique is a promising low-cost candidate not only for human tracking but also for other applications such as human gait analysis.