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Volatile organic compounds (VOC) are considered a class of pollutants with a significant presence in indoor and outdoor air and serious health effects. The aim of this study was to measure and evaluate the levels of outdoor and indoor VOCs at selected sites on Rhodes Island, Greece, during the cold and warm periods of 2023. Spatial and seasonal variations were evaluated; moreover, cancer and non-cancer inhalation risks were assessed. For this purpose, simultaneous indoor-outdoor air sampling was carried out on the island of Rhodes. VOCs were determined by Thermal Desorption-Gas Chromatography/Mass Spectroscopy (TD-GC/MS). Fifty-six VOCs with frequencies ≥ 50% were further considered. VOC concentrations (∑56VOCs) at all sites were found to be higher in the warm period. In the warm and cold sampling periods, the highest concentrations were found at the port of Rhodes City, while total VOC concentrations were dominated by alkanes. The Positive Matrix Factorization (PMF) model was applied to identify the VOC emission sources. Non-cancer and cancer risks for adults were within the safe levels.

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The necessity for reliable, standardized and validated fitness to drive assessment tools for older drivers have been highlighted and discussed for over three decades. Existing neuropsychological tests of driving performance are focusing mostly on visuo-spatial attention and executive functioning rather than other senses. Over the last decade, olfactory deterioration has been found to be associated with cognitive decline and predicting transition from mild cognitive impairment to dementia. The AGILE fitness to drive battery is standardized for older drivers. In this study it was adapted to include the olfactory Sniff' and Stick's test. The aim was to investigate the value of relevant deficits as predictive markers of driving ability in three driving groups (older drivers with: (a) no impairment (controls), (b) with Mild Cognitive Impairment (MCI) and (c) MCI and other chronic conditions, i.e., comorbidities). So far, no other study has investigated the predictive value of olfactory deficits in driving ability. The findings revealed that discrimination is important for the first year of the examination and as the decline progresses, identification becomes the better olfactory marker. The latter is also evident in the literature. Hence, the results showed that less indicators are required compared to the initial battery. The olfactory markers were dominant over the neuropsychological tests, apart from alertness, for predicting the older driver's fitness to drive regardless of the presence of cognitive impairment and other chronic conditions.

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Over the past two decades, several studies have measured olfactory performance in Mild Cognitive Impairment (MCI). Deficits are observed in multiple olfactory domains, including odour detection threshold, identification, discrimination, and memory. In this study, the psychophysiological Sniffin' Sticks smell screening test was administered to examine olfactory functioning in 145 older adults with MCI, a group with MCI and chronic comorbid conditions, and a healthy age-matched comparison group. We hypothesised that olfactory performance will deteriorate in the two MCI groups compared to the control group, even after assessing the known contributions of age and gender. The higher olfactory deterioration in the group with the MCI and the comorbidities in the first year disappeared in the second. This could mean that early consideration of the potential effect of other comorbidities that might affect olfaction should be taken and addressed, as they could easily mask the effect of cognitive decline and/or contribute to it. This study also found higher deterioration in smell identification in participants with MCI, as has been found repeatedly in similar research. Olfactory identification seems to be a more robust marker for discriminating people with MCI and without, and even discriminating between those with MCI and having other health problems.

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AIM: The study describes the driving habits of people with cognitive disorders and previous professional driving experience. A similar study has not been mentioned in the literature. METHOD: From a total of 639 drivers who participated in the research, 153 participants were selected based on their answer on an extensive driving questionnaire. They were asked whether they had a professional driving license. Forty-three participants (28.1%) said "Yes", 110 participants (71.9%) said "No". Out of the 153 participants, 55 (35.9%) were diagnosed with Alzheimer's disease (AD), 44 (28.8%) with Mild Cognitive Impairment (MCI), and 54 (35.3%) were healthy. Additionally, 31 professional drivers were compared to 31 non-professional drivers (N=62) on a short driving questionnaire. The distribution of the population according to the diagnosis was: 18 (29%) with MCI (N=9 professionals, N=9 non-professionals), 30 (48.4%) with AD (N=15 professionals, N=15 non-professionals), 14 (22.6%) healthy (N=7 professionals, N=7 non-professionals). Professional and non-professional drivers were randomly selected to match in terms of diagnosis, age, gender and years of education. The AD diagnosis was based on the NINCDS-ARDRA criteria while the MCI diagnosis was based on the Petersen and Winblad criteria. Healthy older adults were examined across the same neuropsychological battery. RESULTS: The extensive driving questionnaire showed that more non-professional AD drivers (21.9%) had not renewed their license compared to professionals (p=0.048). More non-professional MCI drivers (91.7%) travelled fewer kilometers compared to professionals (p=0.029). Taking both MCI (27.6%) and AD patients (63.2%) together, more non-professional drivers always avoided driving in unfamiliar areas compared to professionals, MCI (p=0.045) and AD (p=0.026). Finally, more non-professional AD drivers (80.0%) avoided driving when it snowed compared to professionals (p=0.34). The short driving questionnaire showed that healthy non-professional drivers almost always (85.7%) avoided turning into difficult intersections compared to professional drivers (14.3%) (p=0.001). CONCLUSION: People with cognitive disorders and previous professional experience were better at driving than the inexperienced ones.

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Older people with cognitive impairment need support in their everyday living. IN LIFE an EC co-funded project aims to organize large-scale and multi-country pilot applications, by providing interoperable, open, personalised and seamless ICT solutions that support older persons in all key home activities, communication, health maintenance, travel, mobility and socialisation tasks, with novel, scalable and viable business models.

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This paper is presenting the efforts to implement in real time and for on-board applications a set of Driver-Vehicle-Environment (DVE) monitoring modules based on the theoretical work done in DVE modelling within the EC 6th FW co funded AIDE Integrated Project. First the need for such an implementation will be discussed. Then the basic DVE modelling principles will be introduced and analysed. Based on that and on the overview of the theoretical work performed around the DVE modelling, the real time DVE monitoring modules developed in this project will be presented and analysed. To do this the DVE parameters needed to allow the required functionalities will be discussed and analysed. Special attention will be given to the use cases and scenarios of use for the real time DVE modules. This allows the reader to understand the functionalities that these modules enable in tomorrow's vehicles that will integrate a large degree of automation supported by advanced integrated and adaptive human machine interfaces (HMIs). The paper will also present examples of the functional and technical tests and validation results for some of the DVE modules. The paper will conclude with a discussion around the lessons learned about the design and implementation of such systems. This will include also the next steps and open issues for research in order for these systems to become standard modules in tomorrow's vehicles.

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Driver sleepiness due to sleep deprivation is a causative factor of many road accidents. Reducing the extent of the sleepy driving problem by developing a countermeasure device that will monitor the sleepiness level of the driver is crucial to improve the safety of the roads. Among numerous physiological measurements, the electroencephalographic (EEG) signal seems to be the most sensitive to detect sleepiness. Previous studies in the field have found consistent alterations of EEG signal during sleepy driving, though they face methodological limitations. We present here preliminary results from a real-driving experiment in which a more complete experimental setup was followed. The subjects were exposed to driving conditions twice: once after they had a normal sleep during the previous night, and once after they remained awake for at least 24 hours prior to the experiment. Significant alterations were observed in the alpha and beta EEG frequencies bands between the two sessions. Electroopthalmographic (EOG) measurements revealed an increased number of eye blinking during the sleep-deprived session in comparison to the control condition. Both measurements can be used for the successful design of a sleepiness detection countermeasure device.

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OBJECTIVE: The objective of this study is the development and evaluation of efficient neurophysiological signal statistics, which may assess the driver's alertness level and serve as potential indicators of sleepiness in the design of an on-board countermeasure system. METHODS: Multichannel EEG, EOG, EMG, and ECG were recorded from sleep-deprived subjects exposed to real field driving conditions. A number of severe driving errors occurred during the experiments. The analysis was performed in two main dimensions: the macroscopic analysis that estimates the on-going temporal evolution of physiological measurements during the driving task, and the microscopic event analysis that focuses on the physiological measurements' alterations just before, during, and after the driving errors. Two independent neurophysiologists visually interpreted the measurements. The EEG data were analyzed by using both linear and non-linear analysis tools. RESULTS: We observed the occurrence of brief paroxysmal bursts of alpha activity and an increased synchrony among EEG channels before the driving errors. The alpha relative band ratio (RBR) significantly increased, and the Cross Approximate Entropy that quantifies the synchrony among channels also significantly decreased before the driving errors. Quantitative EEG analysis revealed significant variations of RBR by driving time in the frequency bands of delta, alpha, beta, and gamma. Most of the estimated EEG statistics, such as the Shannon Entropy, Kullback-Leibler Entropy, Coherence, and Cross-Approximate Entropy, were significantly affected by driving time. We also observed an alteration of eyes blinking duration by increased driving time and a significant increase of eye blinks' number and duration before driving errors. CONCLUSIONS: EEG and EOG are promising neurophysiological indicators of driver sleepiness and have the potential of monitoring sleepiness in occupational settings incorporated in a sleepiness countermeasure device. SIGNIFICANCE: The occurrence of brief paroxysmal bursts of alpha activity before severe driving errors is described in detail for the first time. Clear evidence is presented that eye-blinking statistics are sensitive to the driver's sleepiness and should be considered in the design of an efficient and driver-friendly sleepiness detection countermeasure device.

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Fully immersive and stereoscopic Virtual Environments (VE) represent a powerful multimedia tool for laboratory-based simulations of distinct scenarios including scenarios for evaluating stressful situations resembling reality. Thus far, cortisol secretion as a neuroendocrine parameter of stress has not been evaluated within a Virtual Reality (VR)-based paradigm. In this study 94 healthy volunteers were subjected to a provocative VR-paradigm and a cognitive stress task. Provocative in this context means the VE was designed to provoke physiological reactions (cortisol secretion) within the respective users by purpose. It was tested (a) if a fully dynamic VE as opposed to a static VE can be regarded as a stressor and (b) if such a fully dynamic VE can modify an additional response to a cognitive stressor presented within the VE additionally. Furthermore, possible gender-related impacts on cortisol responses were assessed. A significant cortisol increase was observed only after the combined application of the fully dynamic VE and the cognitive stressor, but not after application of the dynamic VE or the cognitive stressor alone. Cortisol reactivity was greater for men than for women. We conclude that a fully dynamic VE does not affect cortisol secretion per se, but increases cortisol responses to a dual task paradigm that includes performance of a stressful mental task. This provides the basis for the application of VR-based technologies in neuroscientific research, including the assessment of the human Hypothalamus-Pituitary-Adrenal (HPA) axis regulation.

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In today's society, there is an increasing number of workplaces in virtual environments (VE). But, there are only a few reports dealing with occupational health issues or age effects. The question arises how VR generally interferes with cognitive processes. This interference might have relevant implications for workability and work-efficiency in virtual environments. Event-related potentials are known to reflect different stages of stimulus reception, evaluation, and response. We have established an electroencephalographic (EEG) monitoring, focussing on event-related potentials (N100; mismatch negativity, i.e., MMN) to obtain access to attention dependent and pre-attentive processing of sensory stimuli applied in VE. The MMN is known to be correlated with the ability of subjects to react to an unexpected event. The aim of the present study was to investigate cognitive responses to distracting auditory stimuli in two different age groups in a virtual environment (VE) and in a real environment ("real reality"), and to compare characteristic neurophysiological response patterns. Data show that stimulus detection as given by the N100 amplitude and latency does not differ in both age groups and task conditions. In contrast, the pre-attentive processing as given by the MMN is altered in the VR such as the non-VR condition in an age-related manner. A relevant finding of the present study was that the age related differences seen in the non-VR condition were not strengthened in VR.

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For patients suffering from mild cognitive impairments, the navigation through a virtual maze should be as intuitive and efficient as possible in order to minimize cognitive and physical strain. This paper discusses the appropriateness of interaction devices for being used for easy navigation tasks. Information gained from human centered evaluation was used to develop an intuitive and ergonomic interaction device. Two experiments examined the usability of tracked interaction devices. Usability problems with the devices are discussed. The findings from the experiments were translated into general design guidance, in addition to specific recommendations. A new device was designed on the basis of these recommendations and its usability was evaluated in a second experiment. The results were used to develop a lightweight interaction device for navigation in the virtual maze.

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