A methodology of studying of ingestive behavior by non-invasive monitoring of swallowing (deglutition) and chewing (mastication) has been developed. The target application for the developed methodology is to study the behavioral patterns of food consumption and producing volumetric and weight estimates of energy intake. Monitoring is non-invasive based on detecting swallowing by a sound sensor located over laryngopharynx or by a bone conduction microphone and detecting chewing through a below-the-ear strain sensor. Proposed sensors may be implemented in a wearable monitoring device, thus enabling monitoring of ingestive behavior in free living individuals. In this paper, the goals in the development of this methodology are two-fold. First, a system comprised of sensors, related hardware and software for multimodal data capture is designed for data collection in a controlled environment. Second, a protocol is developed for manual scoring of chewing and swallowing for use as a gold standard. The multi-modal data capture was tested by measuring chewing and swallowing in twenty one volunteers during periods of food intake and quiet sitting (no food intake). Video footage and sensor signals were manually scored by trained raters. Inter-rater reliability study for three raters conducted on the sample set of 5 subjects resulted in high average intra-class correlation coefficients of 0.996 for bites, 0.988 for chews, and 0.98 for swallows. The collected sensor signals and the resulting manual scores will be used in future research as a gold standard for further assessment of sensor design, development of automatic pattern recognition routines, and study of the relationship between swallowing/chewing and ingestive behavior.

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Fingerprint recognition systems are vulnerable to artificial spoof fingerprint attacks, like molds made of silicone, gelatin or Play-Doh. "Liveness detection", which is to detect vitality information from the biometric signature itself, has been proposed to defeat these kinds of spoof attacks. The goal for the LivDet 2009 competition is to compare different methodologies for softwarebased fingerprint liveness detection with a common experimental protocol and large dataset of spoof and live images. This competition is open to all academic and industrial institutions which have a solution for software-based fingerprint vitality detection problem. Four submissions resulted in successful completion: Dermalog, ATVS, and two anonymous participants (one industrial and one academic). Each participant submitted an algorithm as a Win32 console application. The performance was evaluated for three datasets, from three different optical scanners, each with over 1500 images of "fake" and over 1500 images of "live" fingerprints. The best results were from the algorithm submitted by Dermalog with a performance of 2.7% FRR and 2.8% FAR for the Identix (L-1) dataset. The competition goal is to become a reference event for academic and industrial research in software-based fingerprint liveness detection and to raise the visibility of this important research area in order to decrease risk of fingerprint systems to spoof attacks.

This study identified several factors that need to be controlled and/or isolated in order to successfully record EEG features that index pain-related activity in the somatosensory cortices.

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Our understanding of etiology of obesity and overweight is incomplete due to lack of objective and accurate methods for Monitoring of Ingestive Behavior (MIB) in the free living population. Our research has shown that frequency of swallowing may serve as a predictor for detecting food intake, differentiating liquids and solids, and estimating ingested mass. This paper proposes and compares two methods of acoustical swallowing detection from sounds contaminated by motion artifacts, speech and external noise. Methods based on mel-scale Fourier spectrum, wavelet packets, and support vector machines are studied considering the effects of epoch size, level of decomposition and lagging on classification accuracy. The methodology was tested on a large dataset (64.5 hours with a total of 9,966 swallows) collected from 20 human subjects with various degrees of adiposity. Average weighted epoch recognition accuracy for intra-visit individual models was 96.8% which resulted in 84.7% average weighted accuracy in detection of swallowing events. These results suggest high efficiency of the proposed methodology in separation of swallowing sounds from artifacts that originate from respiration, intrinsic speech, head movements, food ingestion, and ambient noise. The recognition accuracy was not related to body mass index, suggesting that the methodology is suitable for obese individuals. Index Terms NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript I IntroductionThe world is still losing in the battle with the obesity epidemic. According to WHO, in 2005 there were approximately 1.6 billion overweight and at least 400 million obese adults worldwide [1]. Current trends are unsettling: 2015 projections predict 2.3 billion overweight and 700 million obese adults worldwide. Obesity is one of the risk factors for development of chronic diseases and presents a serious health problem. A recent study [2] suggested that effects of obesity on global health may be comparable to those of cancer. Though the etiology of obesity is a topic of ongoing scientific debate, regulation of food intake may be an important factor for maintaining a healthy weight [3] in the environment that provides abundance of inexpensive, highly palatable and energy dense foods, while requiring only minimal levels of physical activity [4].While various methods have been developed for accurate and objective characterization of physical activity [5], at the present time, there is no accurate, inexpensive, non-intrusive way for objective Monitoring of Ingestive Behavior (MIB) in free living conditions. The most precise method of measuring energy intake is the Doubly-Labeled Water (DLW) technique which provides accurate estimates of caloric energy intake over long periods of time (10-14 days), if subjects remain weight stable. However, the DLW technique cannot identify daily intake patterns. Dietary self-report methods like food frequency questionnaires [6], selfreported diet diaries [7], and multimedia diaries [8] have been shown to be ...