Investigating Cardiovascular Activation of Young Adults in Routine Driving

We report on a naturalistic study investigating the effects of routine driving on cardiovascular activation. We recruited 21 healthy young adults from a broad geographic area in the Southwestern United States. Using the participants' own smartphones and smartwatches, we monitored for a week both their driving and non-driving activities. Monitoring included the continuous recording of a) heart rate throughout the day, b) hand motion during driving as a proxy of persistent texting, and c) contextualized driving data, complete with traffic and weather information. These high temporal resolution variables were complemented with the drivers' biographic and psychometric profiles. Our analysis suggests that anxiety predisposition and high speeds are associated with significant cardiovascular activation on drivers, likely linked to sympathetic arousal. Surprisingly, these associations hold true under good weather, normal traffic, and with experienced drivers behind the wheel. The said findings call for attention to insidious effects of apparently benign drives even for people in their prime. Accordingly, our research contributes to intriguing new discourses on driving affect and personal health informatics

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

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

Bayesian Multimodal Data Analytics: AnIntroduction

Bayesian methods for multimodal data have attracted the interest of re-searchers and practitioners in a variety of real-world applications. In-deed, Bayesian statistics provide an effective framework to deal with mix-tures of unimodal distributions allowing one to incorporate prior infor-mation, when available, and to model posterior distribution in distinct modes. This introductory chapter presents a brief overview of the Bayes-ian perspective in the field of multimodal data, as well as a brief over-view of salient applications. This chapter additionally offers the reader an introduction to two subsequent studies, wherein Bayesian modeling methods are presented for addressing multimodal data in the context of risk analysis and gestural human-machine interaction problems, respec-tively

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