Toward objective monitoring of ingestive behavior in free-living population.

Understanding of eating behaviors associated with obesity requires objective and accurate monitoring of food intake patterns. Accurate methods are available for measuring total energy expenditure and its components in free-living populations, but methods for measuring food intake in free-living people are far less accurate and involve self-reporting or subjective monitoring. We suggest that chews and swallows can be used for objective monitoring of ingestive behavior. This hypothesis was verified in a human study involving 20 subjects. Chews and swallows were captured during periods of quiet resting, talking, and meals of varying size. The counts of chews and swallows along with other derived metrics were used to build prediction models for detection of food intake, differentiation between liquids and solids, and for estimation of the mass of ingested food. The proposed prediction models were able to detect periods of food intake with >95% accuracy and a fine time resolution of 30 s, differentiate solid foods from liquids with >91% accuracy, and predict mass of ingested food with >91% accuracy for solids and >83% accuracy for liquids. In earlier publications, we have shown that chews and swallows can be captured by noninvasive sensors that could be developed into a wearable device. Thus, the proposed methodology could lead to the development of an innovative new way of assessing human eating behavior in free-living conditions.

On techniques for angle compensation in nonideal iris recognition.

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

Sleep state scoring in infants from respiratory and activity measurements.

Sleep state scoring usually relies on polysomnographic measurements, which include electroencephalogram (EEG), electromyogram (EMG), electro-oculogram (EOG), two or three lead chest electrocardiogram (ECG), and may include other measurements. Overall, polysomnography is an intrusive procedure not well tolerated by infants and elderly. The goal of this research is to study possibility of automatic sleep state scoring from less intrusive measurements such as activity measurements and respiratory measurements by inductive plethysmography. The study is based on the Collaborative Home Infant Monitoring Evaluation (CHIME) dataset. Results demonstrate that the suggested approach is capable of scoring sleep states (awake, rapid eye movement and quiet sleep) with good accuracy.