Mental workload assessment for UAV traffic control using consumer-grade BCI equipment

The increasing popularity of unmanned aerial vehicles (UAVs) in critical applications makes supervisory systems based on the presence of human in the control loop of crucial importance. In UAV-traffic monitoring scenarios, where human operators are responsible for managing drones, systems flexibly supporting different levels of autonomy are needed to assist them when critical conditions occur. The assessment of UAV controllers' performance thus their mental workload may be used to discriminate the level and type of automation required. The aim of this paper is to build a mental-workload prediction model based on UAV operators' cognitive demand to support the design of an adjustable autonomy supervisory system. A classification and validation procedure was performed to both categorize the cognitive workload measured by ElectroEncephaloGram signals and evaluate the obtained patterns from the point of view of accuracy. Then, a user study was carried out to identify critical workload conditions by evaluating operators' performance in accomplishing the assigned tasks. Results obtained in this study provided precious indications for guiding next developments in the field

Bezpieczeństwo bezzałogowych systemów powietrznych w środowisku zakłóceń

This study considers signal interference as a form of non-kinetic warfare against unmanned aircraft systems (UAS). The practical purpose of this paper is to organize the theoretical knowledge and terminology concerning interference, while its cognitive purpose is to demonstrate that this recently recognized problem is a real threat to the safe use of unmanned aircraft systems. The research methods employed in this work – analysis, synthesis, abstracting and generalization – serve to determine the types of interference (i.e. primarily jamming and spoofing) and their characteristics in relation to unmanned aircraft. The paper organizes the theoretical knowledge by explaining the signal interference techniques and indicating the targets of attacks or objects of interest. Therefore, available non-kinetic energy UAS neutralization systems are analyzed with respect to their capabilities, tactical characteristics and technical specifications. Given the anticipated further development of unmanned aircraft systems technologies, signal interference carries serious implications for the safety of autonomous operation of these systems. The second important conclusion highlighted in this analysis is the absence of a legislative framework that would regulate the use of interference facilities. This gap in legal regulations implicitly permits their engagement against such crucial infrastructure as e.g. satellite devices for navigation systems – standard and critical equipment in the aviation.Przedmiotem badań tego opracowania są zakłócenia jako forma niekinetycznej walki z bezzałogowymi systemami powietrznymi. Celem praktycznym artykułu jest uporządkowanie teoretycznej wiedzy i terminologii dotyczącej zakłóceń, celem poznawczym natomiast jest wykazanie, że zakłócenia stanowią realny i nowy problemem, dotyczący bezpieczeństwa użytkowania bezzałogowych systemów powietrznych. W pracy użyto teoretycznych metod badawczych, takich jak: analiza, synteza, abstrahowanie i uogólnienie. Rezultatem badań jest określenie rodzajów zakłóceń (głównie jamming oraz spoofing) oraz ich charakterystyki w stosunku do bezzałogowych aparatów latających. Dokonano uporządkowania wiedzy teoretycznej, wyjaśniając techniki zakłóceń, wskazując na cele ataków oraz obiekty ich oddziaływania. W pracy dokonano analizy możliwości dostępnych, niekinetycznych systemów neutralizacji BSP wraz z ich możliwościami oraz charakterystyką taktyczno-techniczną. Przewidując dalszy rozwój bezzałogowych systemów powietrznych wskazano na problem bezpieczeństwa w kontekście autonomizacji tych systemów. Drugim ważnym wnioskiem jest brak norm prawnych regulujących używanie urządzeń do zakłócania. Stwarza to realny problem, pozwalając na ich użytkowanie np. w stosunku do urządzeń satelitarnych systemów nawigacyjnych, stosowanych powszechnie w lotnictwie

Exploring Machine Learning Approaches for Classifying Mental Workload using fNIRS Data from HCI Tasks

Functional Near-Infrared Spectroscopy (fNIRS) has shown promise for being potentially more suitable (than e.g. EEG) for brain-based Human Computer Interaction (HCI). While some machine learning approaches have been used in prior HCI work, this paper explores different approaches and configurations for classifying Mental Workload (MWL) from a continuous HCI task, to identify and understand potential limitations and data processing decisions. In particular, we investigate three overall approaches: a logistic regression method, a supervised shallow method (SVM), and a supervised deep learning method (CNN). We examine personalised and gen-eralised models, as well as consider different features and ways of labelling the data. Our initial explorations show that generalised models can perform as well as personalised ones and that deep learning can be a suitable approach for medium size datasets. To provide additional practical advice for future brain-computer interaction systems, we conclude by discussing the limitations and data-preparation needs of different machine learning approaches. We also make recommendations for avenues of future work that are most promising for the machine learning of fNIRS data

Human-Machine Interfaces for Service Robotics

L'abstract è presente nell'allegato / the abstract is in the attachmen

Exploring the use of brain-sensing technologies for natural interactions

Recent technical innovation in the field of Brain-Computer Interfaces (BCIs) has increased the opportunity for including physical, brain-sensing devices as a part of our day-to-day lives. The potential for obtaining a time-correlated, direct, brain-based measure of a participant's mental activity is an alluring and important development for HCI researchers. In this work, we investigate the application of BCI hardware for answering HCI centred research questions, in turn, fusing the two disciplines to form an approach we name - Brain based Human-Computer Interaction (BHCI). We investigate the possibility of using BHCI to provide natural interaction - an ideal form of HCI, where communication between man-and-machine is indistinguishable from everyday forms of interactions such as Speaking and Gesturing. We present the development, execution and output of three user studies investigating the application of BHCI. We evaluate two technologies, fNIRS and EEG, and investigate their suitability for supporting BHCI based interactions. Through our initial studies, we identify that the lightweight and portable attributes of EEG make it preferable for use in developing natural interactions. Building upon this, we develop an EEG based cinematic experience exploring natural forms of interaction through the mind of the viewer. In studying the viewers response to this experience, we were able to develop a taxonomy of control based on how viewers discovered and exerted control over the experience

Exploring the use of brain-sensing technologies for natural interactions

Recent technical innovation in the field of Brain-Computer Interfaces (BCIs) has increased the opportunity for including physical, brain-sensing devices as a part of our day-to-day lives. The potential for obtaining a time-correlated, direct, brain-based measure of a participant's mental activity is an alluring and important development for HCI researchers. In this work, we investigate the application of BCI hardware for answering HCI centred research questions, in turn, fusing the two disciplines to form an approach we name - Brain based Human-Computer Interaction (BHCI). We investigate the possibility of using BHCI to provide natural interaction - an ideal form of HCI, where communication between man-and-machine is indistinguishable from everyday forms of interactions such as Speaking and Gesturing. We present the development, execution and output of three user studies investigating the application of BHCI. We evaluate two technologies, fNIRS and EEG, and investigate their suitability for supporting BHCI based interactions. Through our initial studies, we identify that the lightweight and portable attributes of EEG make it preferable for use in developing natural interactions. Building upon this, we develop an EEG based cinematic experience exploring natural forms of interaction through the mind of the viewer. In studying the viewers response to this experience, we were able to develop a taxonomy of control based on how viewers discovered and exerted control over the experience

The 45th Australasian Universities Building Education Association Conference: Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment, Conference Proceedings, 23 - 25 November 2022, Western Sydney University, Kingswood Campus, Sydney, Australia

This is the proceedings of the 45th Australasian Universities Building Education Association (AUBEA) conference which will be hosted by Western Sydney University in November 2022. The conference is organised by the School of Engineering, Design, and Built Environment in collaboration with the Centre for Smart Modern Construction, Western Sydney University. This year’s conference theme is “Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment”, and expects to publish over a hundred double-blind peer review papers under the proceedings

Measuring knowledge sharing processes through social network analysis within construction organisations

The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes