Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting.

Several specialized retinal optical coherence tomography (OCT) acquisition and processing methods have been recently developed to allow in vivo probing of light-evoked photoreceptors function, focusing on measurements in individual photoreceptors (rods and cones). Recent OCT investigations in humans and experimental animals have shown that the outer segments in dark-adapted rods and cones elongate in response to the visible optical stimuli that bleach fractions of their visual photopigment. We have previously successfully contributed to these developments by implementing OCT intensity-based “optoretinograms” (ORG), the paradigm of using near-infrared OCT (NIR OCT) to measure bleaching-induced back-scattering and/or elongation changes of photoreceptors in the eye in vivo. In parallel, several groups have successfully implemented phase-based ORGs, mainly in human studies, exploiting changes in the phases of back-scattered light. This allowed more sensitive observations of tiny alterations of photoreceptors structures. Applications of the phase-based ORG have been implemented primarily in high speed and cellular resolution AO-OCT systems that can visualize photoreceptor mosaic, allowing phase measurements of path length changes in outer segments of individual photoreceptors. The phase-based ORG in standard resolution OCT systems is much more demanding to implement and has not been explored extensively. This manuscript describes our efforts to implement a phase analysis framework to retinal images acquired with a standard resolution and raster scanning OCT system, which offers much lower phase stability than line-field or full-field OCT detection schemes due to the relatively slower acquisition speed. Our initial results showcase the successful extraction of phase-based ORG signal from the B-scans acquired at ∼100 Hz rate and its favorable comparison with intensity-based ORG signal extracted from the same data sets. We implemented the calculation of phase-based ORG signals using Knox-Thompson paths and modified signal recovery by adding decorrelation weights. The phase-sensitive ORG signal analysis developed here for mouse retinal raster scanning OCT systems could be in principle extended to clinical retinal raster scanning OCT systems, potentially opening doors for clinically friendly ORG probing.

We introduce a novel, noninvasive retinal eye-tracking system capable of detecting eye displacements with an angular resolution of 0.039 arcmin and a maximum velocity of 300°/s across an 8° span. Our system is designed based on a confocal retinal imaging module similar to a scanning laser ophthalmoscope. It utilizes a 2D MEMS scanner ensuring high image frame acquisition frequencies up to 1.24 kHz. In contrast with leading eye-tracking technology, we measure the eye displacements via the collection of the observed spatial excursions for all the times corresponding a full acquisition cycle, thus obviating the need for both a baseline reference frame and absolute spatial calibration. Using this approach, we demonstrate the precise measurement of eye movements with magnitudes exceeding the spatial extent of a single frame, which is not possible using existing image-based retinal trackers. We describe our retinal tracker, tracking algorithms and assess the performance of our system by using programmed artificial eye movements. We also demonstrate the clinical capabilities of our system with in vivo subjects by detecting microsaccades with angular extents as small as 0.028°. The rich kinematic ocular data provided by our system with its exquisite degree of accuracy and extended dynamic range opens new and exciting avenues in retinal imaging and clinical neuroscience. Several subtle features of ocular motion such as saccadic dysfunction, fixation instability and abnormal smooth pursuit can be readily extracted and inferred from the measured retinal trajectories thus offering a promising tool for identifying biomarkers of neurodegenerative diseases associated with these ocular symptoms.

A decline in the Spatial Navigation (SN) abilities has been observed in the course of healthy aging. Walking is an inseparable part of the navigation process; however, research tasks overlook this aspect in studies involving seniors. The present study was designed to overcome this limitation by recording gait parameters during natural environment navigation and to determine gait indicators that most accurately assign the participants to the proper age category. Thirteen elderly (mean age = 69.1 ± 5.4 year) and sixteen young women (mean age = 21.5 ± 2.2 year) equipped with gait sensors were asked to learn a path while walking in a real building (Learning Phase), reproduce the path (Memory Phase) and reach targets after a 30 min delay (Delayed Phase). The Receiver Operating Characteristics (ROC) analysis showed that our self-developed Gait Style Change indicator, that is, the difference in the probability of feet landing between particular SN task phases, classified the participants into either the elderly or the young group with the highest accuracy (0.91). The second most important indicator, the Task-Related (step counts in each SN task phase), achieved the accuracy discrimination of 0.83. The gait indicators, comprising single gait parameters measured while navigating, might be considered as accurately differentiating older from younger people.

We propose an algorithm for map-aided inertial dead reckoning (DR) for indoor navigation. The technique is based on a concept of collision avoidance velocity. New positions estimated by DR are checked for collision detection with walls and if such a situation is detected, the collision avoidance velocity is calculated to avert the obstacles. The differences between an individual’s indicated velocity and collision avoidance velocity are then used to correct errors for further path development. The best performance of the proposed technique is achieved when the individual moves along building obstacles. This type of movement is typical for firefighters, during search and rescue operations in thick smoke. Such conditions provided the environment for testing our algorithm.