Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities.

We describe a controlled experiment, aiming to study productivity and stress effects of email interruptions and activity interactions in the modern office. The measurement set includes multimodal data for n = 63 knowledge workers who volunteered for this experiment and were randomly assigned into four groups: (G1/G2) Batch email interruptions with/without exogenous stress. (G3/G4) Continual email interruptions with/without exogenous stress. To provide context, the experiment's email treatments were surrounded by typical office tasks. The captured variables include physiological indicators of stress, measures of report writing quality and keystroke dynamics, as well as psychometric scores and biographic information detailing participants' profiles. Investigations powered by this dataset are expected to lead to personalized recommendations for handling email interruptions and a deeper understanding of synergistic and antagonistic office activities. Given the centrality of email in the modern office, and the importance of office work to people's lives and the economy, the present data have a valuable role to play

Multi-Spectral Face Recognition- Fusion of Visual Imagery with Physiological Information

Summary. We present a novel multi-spectral approach for face recognition using visual imagery as well as the physiological information extracted from thermal facial imagery. The main point of this line of research is that physiological information available only in thermal infrared, can improve the performance and enhance the capabilities of standard visual face recognition methods. For each subject in the database, we store facial images collected simultaneously in the visual and thermal bands. For each of the thermal images, we first delineate the human face from the background using the Bayesian framework. We then extract the blood vessels present on the segmented facial tissue using image morphology. The extracted vascular network produces contour shapes that are unique to each individual. The branching points of the skeletonized vascular network, referred to as thermal minutia points (TMPs), are an effective feature abstraction. During the classification stage, we match the local and global structures of TMPs extracted from the test image with those of the corresponding images in the database. We fuse the recognition results of our thermal imaging algorithm with those of a popular visual imaging algorithm. We have conducted experiments on a large database of co-registered visual and thermal facial images. The good experimental results show that the proposed fusion approach has merit and promise.

Face Recognition Beyond the Visible Spectrum

Abstract. The facial vascular network is highly characteristic to the individual, much like the way his fingerprint is. A non-obtrusive way to capture this information is through thermal imaging. The convective heat transfer effect from the flow of “hot ” arterial blood in superficial vessels creates characteristic thermal imprints, which are at a gradient with the surrounding tissue. This casts sigmoid edges on the human tissue where major blood vessels are present. We present an algorithmic methodology to extract and represent the facial vasculature. The methodology combines image morphology and probabilistic inference. The morphology captures the overall structure of the vascular network while the probabilistic part reflects the positional uncertainty for the vessel walls, due to the phenomenon of thermal diffusion. The accuracy of the methodology is tested through extensive experimentation and meticulous ground-truthing. Furthermore, the efficacy of this information for identity recognition is tested on substantial databases.

On enhancing cardiac pulse measurements through thermal imaging

This paper presents methodological advances on pulse measurement through thermal imaging of the face - a modality that recovers thermo-physiological function. Two previous methods that capitalized on heat transfer effects along and across the vessel during pulse propagation have been brought together in a fusion scheme. In addition, the quality of the extracted physiological signals has improved thanks to sophisticated tracking and noise reduction algorithms. Finally, parameter optimization has fine-tuned harmonic analysis of the signals, thus, strengthening the measurement accuracy. Comparative experiments that were conducted on a dataset of 12 subjects highlighted the virtues of the new methodology versus the legacy ones. Specifically, the new method reduced the instantaneous measurement error from 10.5% to 7.8%, while it improved mean accuracy from 88.6% to 95.3%. This advancement brings clinical applications of the technology within sight. ©2009 IEEE

Methodological advances on pulse measurement through functional imaging

The blood pressure and velocity rise rapidly as a result of the opening of the aortic valve in early systole. This spike in blood pressure and momentum travels the length of the aorta and is passed on to peripheral arteries such as the brachial, the carotid, and beyond. The thus formed pulse is an example of a traveling wave in a fluid medium that involves transport of mass and heat. The alteration of the electric field that moves the heart\u27s muscle and the thermo-mechanical effects of pulse propagation in the vascular network creates opportunities for measurement across different modalities. The method that is considered to be the gold standard for pulse measurement is electrocardiography (ECG) [12]. It produces crisp results because it focuses on the source (heart). Other commonly used methods, such as piezoelectric probing [4], photoplethysmography [13] and Doppler ultrasound [9], focus on the vascular periphery. One main characteristic of all these methods is that they require contact with the subject. There are clinical applications, however, where a contact-free method is desirable. Such applications usually involve sustained physiological monitoring of patients who are in delicate state or form; examples range from sleep studies to neonatal monitoring. © 2010 Springer-Verlag US

A multimodal dataset for various forms of distracted driving

We describe a multimodal dataset acquired in a controlled experiment on a driving simulator. The set includes data for n = 68 volunteers that drove the same highway under four different conditions: No distraction, cognitive distraction, emotional distraction, and sensorimotor distraction. The experiment closed with a special driving session, where all subjects experienced a startle stimulus in the form of unintended acceleration-half of them under a mixed distraction, and the other half in the absence of a distraction. During the experimental drives key response variables and several explanatory variables were continuously recorded. The response variables included speed, acceleration, brake force, steering, and lane position signals, while the explanatory variables included perinasal electrodermal activity (EDA), palm EDA, heart rate, breathing rate, and facial expression signals; biographical and psychometric covariates as well as eye tracking data were also obtained. This dataset enables research into driving behaviors under neatly abstracted distracting stressors, which account for many car crashes. The set can also be used in physiological channel benchmarking and multispectral face recognition