A Bayesian Approach to Statistical Process Control

The frequentist Shewhart charts have proved valuable for the first stage of quality improvement in many manufacturing settings. However, their statistical foundation is on a model with exactly known process parameters and independent identically distributed process readings. One or more aspects of this foundation are often lacking in real problems. A Bayesian framework allowing an escape from the independence and the known-parameter assumptions provides a conceptually sounder and more effective approach for process control when one moves away from this first idealization of a process

Touchless monitoring of breathing function

We have developed a novel method for noncontact measurement of breathing function. The method is based on statistical modeling of dynamic thermal data captured through an infrared imaging system. The expired air has higher temperature than the typical background of indoor environments (e.g., walls). Therefore, the particles of the expired air emit at a higher power than the background, a phenomenon which is captured as a distinct thermal signature in the infrared imagery. There is significant technical difficulty in computing this signature, however, because the phenomenon is of very low intensity and transient nature. We use an advanced statistical algorithm based on the method of moments and the Jeffrey's divergence measure to address the problem. So far, we were able to compute correctly the breathing waveforms for ten (10) subjects at distances ranging from 6-8 feet. The results were checked against concomitant ground-truth data collected with a traditional contact sensor. The technology is expected to find applications in the next generation of touchless polygraphy and in preventive health care

Periorbital thermal signal extraction and applications

We propose a novel method that locatizes thermal footprint of the facial and ophthalmic arterial-venous complexes in the periorbital area. This footprint is used to extract the mean thermal signal over time (periorbital signal), which is a correlate of the blood supply to the ocular muscle. Previous work demonstrated that the periorbital signal is associated to autonomic responses and it changes significantly upon the onset of instantaneous stress. The present method enables accurate and consistent extraction of this signal. It aims to replace the heuristic segmentation approach that has been used in stress quantification thus far. Applications in computational psychology and particularly in deception detection are the first to benefit from this new technology. We tested the method on thermal videos of 39 subjects who faced stressful interrogation for a mock crime. The results show that the proposed approach has improved the deception classification success rate to 82%, which is 20% higher compared to the previous approach

A Three-State Recursive Sequential Bayesian Algorithm for Biosurveillance

A serial signal detection algorithm is developed to monitor pre-diagnosis and medical diagnosis data pertaining to biosurveillance. The algorithm is three-state sequential, based on Bayesian thinking. It accounts for non-stationarity, irregularity, seasonality, and captures an epidemic serial structural details. At stage n, a trichotomous variable governing the states of an epidemic is defined, and a prior distribution for time-indexed serial readings is set. The technicality consists of finding a posterior state probability based on the observed data history, using the posterior as a prior distribution for stage n + 1 and sequentially monitoring surges in posterior state probabilities. A sensitivity analysis for validation is conducted and analytical formulas for the predictive distribution are supplied for error management purposes. The method is applied to syndromic surveillance data gathered in the United States (U.S.) District of Columbia metropolitan area

Urban surveillance systems: From the laboratory to the commercial world

Research in the surveillance domain was confined for years in the military domain. Recently, as military spending for this kind of research was reduced and the technology matured, the attention of the research and development community turned to commercial applications of surveillance. In this paper we describe a state-of-the-art monitoring system developed by a corporate R&D lab in cooperation with the corresponding security business units. It represents a sizable effort to transfer some of the best results produced by computer vision research into a viable commercial product. Our description spans both practical and technical issues. From the practical point of view we analyze the state of the commercial security market, typical cultural differences between the research ream and the business team and the perspective of the potential users of the technology. These are important issues, that have to be dealt with or the surveillance technology will remain in the lab for a long time. From the technical point of view we analyze our algorithmic and implementation choices. We describe the improve we introduced to the original algorithms reported in the literature in response to some problems that arose during field testing. We also provide extensive experimental results that highlight the strong points and some weaknesses of the prototype system