Description and clinical studies of a device for the instantaneous detection of office-place stress.

Occupational stress is universally experienced and is emerging as a major risk factor for physical and mental illness and a key factor in poor work performance and low job satisfaction. However, the technology does not currently exist to unobtrusively measure occupational stress in real-time. Here, we describe the design and clinical validation of an automated high-definition thermal imaging system that can be used to quantify human stress, remotely and instantaneously. Healthy human subjects underwent a computer-based version of the Stroop-color conflict test, which is a validated stress provocation test, in an experimental office facility. In separate experiments, the same subjects completed a mental arithmetic challenge. The thermal signal associated with stress provocation is near-instantaneous corrugator warming. The stress response was detected in all subjects for all stress-events compared to the respective baselines. Furthermore, there was remarkable inter-individual preservation of the corrugator signal with stress R(2) = 0.96, P< 0.001). High-definition thermal imaging can be used for real-time detection of stress provocation. This technology may prove to be of help in ameliorating office-place stress.

An accelerometer-based earpiece to monitor and quantify physical activity.

BACKGROUND: Physical activity is important in ill-health. Inexpensive, accurate and precise devices could help assess daily activity. We integrated novel activity-sensing technology into an earpiece used with portable music-players and phones; the physical-activity-sensing earpiece (PASE). Here we examined whether the PASE could accurately and precisely detect physical activity and measure its intensity and thence predict energy expenditure. METHODS: Experiment 1: 18 subjects wore PASE with different body postures and during graded walking. Energy expenditure was measured using indirect calorimetry. Experiment 2: 8 subjects wore the earpiece and walked a known distance. Experiment 3: 8 subjects wore the earpiece and 'jogged' at 3.5 mph. RESULTS: The earpiece correctly distinguished lying from sitting/standing and distinguished standing still from walking (76/76 cases). PASE output showed excellent sequential increases with increased in walking velocity and energy expenditure (r2 > .9). The PASE prediction of free-living walking velocity was, 2.5 +/- (SD) 0.18 mph c.f. actual velocity, 2.5 +/- 0.16 mph. The earpiece successfully distinguished walking at 3.5 mph from 'jogging' at the same velocity (P < .001). CONCLUSIONS: The subjects tolerated the earpiece well and were comfortable wearing it. The PASE can therefore be used to reliably monitor free-living physical activity and its associated energy expenditure.

Tissue tracking in thermo-physiological imagery through spatio-temporal smoothing.

Accurate tracking of facial tissue in thermal infrared imaging is challenging because it is affected not only by positional but also physiological (functional) changes. This article presents a particle filter tracker driven by a probabilistic template function with both spatial and temporal smoothing components, which is capable of adapting to abrupt positional and physiological changes. The method was tested on tracking facial regions of subjects under varying physiological and environmental conditions in 12 thermal clips. It demonstrated robustness and accuracy, outperforming other strategies. This new method promises improved performance in a host of biomedical applications that involve physiological measurements on the face, like unobtrusive sleep studies.

Physiology-based face recognition in the thermal infrared spectrum.

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.