Enabling Single-Pilot Operations technological and operative scenarios: a state-of-the-art review with possible cues

Both financial and operational reasons have been given emphasis to the implementation of Single-Pilot Operations in commercial aviation. SPO will involve replacing the first officer with integrated cockpit assistants and support ground stations. This review aims to provide an overview of SPO through a classification of the specific areas of interest. Enabling SPO will require designers to re-modulate the human-automation interface according to the new allocation of functions in the flight deck. However, while technological issues are expected to be overcome in the next future, major attention should be paid on the human factor side

Requirement analysis and sensor specifications – First version

In this first version of the deliverable, we make the following contributions: to design the WEKIT capturing platform and the associated experience capturing API, we use a methodology for system engineering that is relevant for different domains such as: aviation, space, and medical and different professions such as: technicians, astronauts, and medical staff. Furthermore, in the methodology, we explore the system engineering process and how it can be used in the project to support the different work packages and more importantly the different deliverables that will follow the current. Next, we provide a mapping of high level functions or tasks (associated with experience transfer from expert to trainee) to low level functions such as: gaze, voice, video, body posture, hand gestures, bio-signals, fatigue levels, and location of the user in the environment. In addition, we link the low level functions to their associated sensors. Moreover, we provide a brief overview of the state-of-the-art sensors in terms of their technical specifications, possible limitations, standards, and platforms. We outline a set of recommendations pertaining to the sensors that are most relevant for the WEKIT project taking into consideration the environmental, technical and human factors described in other deliverables. We recommend Microsoft Hololens (for Augmented reality glasses), MyndBand and Neurosky chipset (for EEG), Microsoft Kinect and Lumo Lift (for body posture tracking), and Leapmotion, Intel RealSense and Myo armband (for hand gesture tracking). For eye tracking, an existing eye-tracking system can be customised to complement the augmented reality glasses, and built-in microphone of the augmented reality glasses can capture the expert’s voice. We propose a modular approach for the design of the WEKIT experience capturing system, and recommend that the capturing system should have sufficient storage or transmission capabilities. Finally, we highlight common issues associated with the use of different sensors. We consider that the set of recommendations can be useful for the design and integration of the WEKIT capturing platform and the WEKIT experience capturing API to expedite the time required to select the combination of sensors which will be used in the first prototype.WEKI

Multimodal approach for emotion recognition based on simulated flight experiments

The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights ( N f l i g h t s = 13 ) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator ( N p i l o t s = 8 ). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.info:eu-repo/semantics/publishedVersio

Passive BCI in operational environments: insights, recent advances and future trends

this mini-review aims to highlight recent important aspects to consider and evaluate when passive Brain-Computer Interface (pBCI) systems would be developed and used in operational environments, and remarks future directions of their applications

Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions

Nanobiosensors: towards real-time human monitoring in aerospace medicine and other extreme conditions

Οι βιοαισθητήρες είναι υποσχόμενα εργαλεία προς την επίτευξη παρακολούθησης της ανθρώπινης υγείας και επίδοσης σε πραγματικό χρόνο και στο σημείο της παρεχόμενης φροντίδας. Η νανοτεχνολογία μπορεί να καταλύσει την διαδικασία της σμίκρυνσης των βιοαισθητήρων ή μπορεί να χρησιμοποιηθεί για την επινόηση εντελώς νέων τύπων αυτών. Η ανάπτυξη των βιοαισθητήρων, σε συνδυασμό με την ωρίμανση της τεχνητής νοημοσύνης και του διαδικτύου των πραγμάτων, μπορεί να εγκαινιάσει μία νέα εποχή για επιτόπου προβλεπτική διαγνωστική, τηλεϊατρική και γενικότερη επαύξηση της επιστημονικής γνώσης. Αυτή η δυναμική είναι άκρως ενδιαφέρουσα για τους κλάδους της ιατρικής που ασχολούνται με την διασφάλιση της ανθρώπινης υγείας, ασφάλειας και επίδοσης σε ακραίες συνθήκες με χαρακτηριστικότερο παράδειγμα τις επανδρωμένες διαστημικές πτήσεις και την ενδεχόμενη πλανητική εποίκιση. Η παρούσα ανασκόπηση επικεντρώνεται σε προσπάθειες βιοανίχνευσης στο διάστημα, αλλά επεκτείνεται και σε περαιτέρω εφαρμογές βιοαισθητήρων στην αεροπορική και την στρατιωτική ιατρική, την αθλητιατρική, καθώς και σε άλλες καταστάσεις με ακραίες περιβαλλοντικές συνθήκες για το ανθρώπινο σώμα. Τέλος, αναφέρονται μερικοί διαφόρου τύπου καινοτόμοι βιοαισθητήρες, με σκοπό να παρασχεθεί μια καλύτερη οπτική για την δυναμική των μελλοντικών συστημάτων βιοαισθητήρων. Αυτή η εργασία έχει ως στόχο να ενθαρρύνει τις επιστημονικές ομάδες που αναπτύσσουν βιοαισθητήρες να συνεργαστούν στενότερα με τους τελικούς χρήστες έτσι, ώστε να επιτευχθεί η απαιτούμενη ποιότητα εκ σχεδιασμού και, δια τούτου, να απελευθερωθεί η πλήρης δυναμική αυτής της επερχόμενης τεχνολογίας.Biosensors are promising tools for achieving point-of-care, real-time, human health, and performance monitoring. Nanotechnology can catalyze the process of biosensors miniaturization or can be used for inventing whole-new types of biosensors. The development of nanobiosensors, along with the maturation of artificial intelligence and Internet-of-Things applications, can inaugurate a new era for in-situ predictive diagnostics, telemedical practice, and general scientific understanding. This potential is of particular interest for medical fields responsible to ensure human health, safety, and performance in extreme environments, with utmost example: manned spaceflight and planets habitation. This review focuses on biosensing approaches in space, but extends further to biosensing applications in aviation, military, and sports, as other situations of extreme environmental conditions for the human body. Lastly, some miscellaneous types of nanobiosensors are mentioned, in order to provide an insight of the potential that future biosensing systems hold. Hopefully, this work will encourage nanobiosensor developers to work closely with the end-users, so that quality-by-design can be achieved, and thus the full potential of this next-generation technology can be harvested

Biosignal-Based Driving Skill Classification Using Machine Learning: A Case Study of Maritime Navigation

This work presents a novel approach to detecting stress differences between experts and novices in Situation Awareness (SA) tasks during maritime navigation using one type of wearable sensor, Empatica E4 Wristband. We propose that for a given workload state, the values of biosignal data collected from wearable sensor vary in experts and novices. We describe methods to conduct a designed SA task experiment, and collected the biosignal data on subjects sailing on a 240° view simulator. The biosignal data were analysed by using a machine learning algorithm, a Convolutional Neural Network. The proposed algorithm showed that the biosingal data associated with the experts can be categorized as different from that of the novices, which is in line with the results of NASA Task Load Index (NASA-TLX) rating scores. This study can contribute to the development of a self-training system in maritime navigation in further studies

M-health review: joining up healthcare in a wireless world

In recent years, there has been a huge increase in the use of information and communication technologies (ICT) to deliver health and social care. This trend is bound to continue as providers (whether public or private) strive to deliver better care to more people under conditions of severe budgetary constraint

Detection of Human Vigilance State During Locomotion Using Wearable FNIRS

Human vigilance is a cognitive function that requires sustained attention toward change in the environment. Human vigilance detection is a widely investigated topic which can be accomplished by various approaches. Most studies have focused on stationary vigilance detection due to the high effect of interference such as motion artifacts which are prominent in common movements such as walking. Functional Near-Infrared Spectroscopy is a preferred modality in vigilance detection due to the safe nature, the low cost and ease of implementation. fNIRS is not immune to motion artifact interference, and therefore human vigilance detection performance would be severely degraded when studied during locomotion. Properly treating and removing walking-induced motion artifacts from the contaminated signals is crucial to ensure accurate vigilance detection. This study compared the vigilance level detection during both stationary and walking states and confirmed that the performance of vigilance level detection during walking is significantly deteriorated (with a