Keywords: Human Machine Systems
Abstract: Mathematical control models are widely used in tuning manual control systems and understanding human performance. The most common model, the crossover model, is severely limited however in describing realistic human control behaviour in relevant control tasks as it is only valid for tracking with a compensatory display. This paper first discusses the state-of- the-art in modelling human control in tracking with pursuit displays. It is shown that, although both tasks seem very similar, the separate presentation of target and system output signals allows operators to adopt a huge variety in control strategies, which makes the development of a universal model for pursuit control a challenge. Two recent models are then described which can act as precursors to this universal model. Third, system identification choices and issues are discussed for pursuit tracking tasks. Finally it is argued that it is inevitable that time-varying rather than time-invariant methods are needed to properly describe human behaviour in the pursuit tracking task, as skilled operators will learn to characterize the probabilistic nature of the task which cannot be captured in a single, linear, time-invariant model.

Keywords: Human Machine Systems, Model-Based Design, Display Design
Abstract: In manual pursuit and preview tracking tasks, humans apply feedforward control to exploit available information of the target trajectory to follow. While the human’s linear, time-invariant dynamics in such tasks are well-understood and have been modeled in the quasi-linear framework, the remaining nonlinear and time-invariant control behavior, the human remnant, is typically ignored. This paper extends the current state-of-the-art theories of human remnant, which are applicable to compensatory tracking tasks only, to the more common and relevant pursuit and preview tracking tasks. Data are presented from three human-in-the-loop tracking experiments. The ratio of the remnant relative to the linear control output is quantified in the frequency domain, and remnant spectra are computed and modeled. The results show that the injected remnant is identical in compensatory, pursuit, and preview tasks, regardless of the task’s controlled element dynamics, preview time, and target trajectory bandwidth. The presented remnant data and models can be used together with already available linear, time-invariant models, to better predict characteristics of human control behavior in pursuit and preview tracking tasks, enabling the design of human assistance systems.

Keywords: Human Machine Systems, Decision Support Systems, Human – Computer Interaction
Abstract: Air Traffic Control (ATC) workload is a limiting factor for air traffic growth, creating a need for objective ATC workload metrics. Previous research has shown that the solution space diagram can be a basis for a workload prediction metric. The current solution space metric however, does not incorporate altitude. In this paper, a 3D solution space metric is described and evaluated. An experiment has been conducted to test the relation of the 3D solution space metric with workload and compare it to other workload metrics; the aircraft count, and a quasi-3D metric: the 2D layered solution space and the Instantaneous Self Assessment-based method. Weak correlations with workload were found for all tested metrics and no significant differences were found between them. Although no significant differences were found, the 2D layered metric showed better results than the 3D solution space-based metric, indicating that air traffic controllers might think in 2D layers over fixed altitude ranges rather than considering the complete 3D physical solution space.

Keywords: Cognitive System Engineering, Decision Support Systems, Human Machine Systems
Abstract: Designing interfaces for eective decision-making supports for complex, dynamic systems is a challenging task. Besides the already challenging task of determining the visual form, the task of defining the content of these supports can be even more demanding. Especially for an unstable and complex work domain with multiple stakeholders and multiple interrelated systems, e.g., commercial ight operations. Various methodologies for designing such supports have been introduced in the last decades. In this paper two methodologies, Ecological Interface Design (EID) and Applied Cognitive Work Analysis (ACWA) are compared to determine what methodology is best suited for the design of an in- flight decision support system. The methodologies are compared on two aspects, (1) development of the knowledge-based model and (2) the means to translate this model into requirements for the actual representation. The functional abstraction network (FAN), as part of the ACWA, is the preferred knowledge-based modelling method for capturing a complex multi-system work domain, like commercial fight operations. Mainly due to the increased flexibility in modeling and ease of extending the model. The ACWA is also found to the preferable method to translate the functional model into representation requirements due to its structured step-wise and system engineering inspired approach.

Keywords: Human Machine Systems, Cognitive System Engineering, Human – Computer Interaction
Abstract: Cyber Defence Exercises (CDX) are common training and learning tools. A recently discussed challenge in cyber defence teaching and training is the gap between the fast technological advancement accompanied by rapidly changing demands on future cyber defence operators, and the lack of science-based teaching and training methods. A growing body of evidence suggests a crucial role of human factors as a central predictor for human performance in sociotechnical systems. While this has been acknowledged in a wide range of safety-critical applied fields, there is still a lack of knowledge about the impact of human factors on cyber defence performance. The lack of conventional metrics of performance and learning progress contribute to this deficit. To address this gap, the Norwegian Defence Cyber Academy (NDCA) follows a science-based educational approach that identified in a series of empirical studies cognitive-psychological predictors for learning success of future cyber defence operators. These predictors and elements of a human factors research program are deeply embedded into educational practice and include processes such as metacognition, self-regulation, coping, communication and shared mental modelling. Slow education methods and mentoring are fundamental to enabling the advancement of human factors cognisance among military cyber cadets. As a tool for efficient training, the NDCA developed and implemented a mentoring concept that involves a cyber defence retrospective timeline analysis involving expert and practitioner level mentors. The timeline differentiates between performance relevant hard- and soft-skills and leads progressively towards an alignment of Security Operation Centre (SOC)- and expert judgments of performance. The NDCA argues that this educational concept facilitates educational benefits based on insight, accurate self-perception, motivation and decreased team workloads following more efficient collaboration.

Keywords: Human – Computer Interaction, Cognitive System Engineering, Decision Support Systems
Abstract: Cybersecurity is a fast growing domain. The supply of workforce entering the labour market can not match the current demands. Due to this currently existing and predicted future skills gap in the labour market, educational institutions attempt to minimize dropouts and study times. As a direct consequence, the relevance of valid admission and selection procedures has grown in recent years. However, there is a mismatch between the increased demand for high-quality admission procedures and the still existing lack of established methods and routines to conduct these. In this paper we discuss our experience from running admissions in one of the oldest European master level cybersecurity curricula in Europe. We argue that cybersecurity skills assessment cannot simply be traditional knowledge-based assessments as this may exclude suitable candidates, who have not had the opportunity to learn the subject matter or are joining from different fields. Also selection decision cannot be done purely based on previous grades, because decomposing school subjects into cybersecurity skills is challenging due to the domain’s interdisciplinary nature. We present a technical skills assessment method using cloud-based virtual labs that can be done by the candidates remotely. Those labs focus on assessing the technical competencies of a candidate and leave the assessment of non-technical skills (which are at least equally important) to a human interviewer. Also identifying cheaters, who do not prepare their labs themselves, will be left for the human interviewer. Such on-line exercises show potential as scalable option to evaluate the cybersecurity technical skills, motivational levels and cognitive strategies applied for problem-solving in a complex, novel task when being under performance pressure. The lessons learned are shared; feedback obtained from the applicants and possible technical metrics for predicting their success in a cybersecurity program are explored. As further work, we plan to conduct full data analysis and time-delayed interviews to generate hypothesis that can be further empirically tested with appropriate designs to detect causal relationships.

Keywords: Human Machine Systems, Human – Computer Interaction, Social Computing
Abstract: Many social-networks today face growing problems of group polarization, radicalization, and fake news. These issues are being exacerbated by the phenomenon of bots, which are becoming better at mimicking real people and are able to spread fake news faster within social-networks. Methods exist for detecting these social-media bots, but they may be vulnerable to manipulation. One way this might be done is through what is called a poisoning attack, where the data used to train a model is altered with the goal of reducing the models accuracy. The goal of this research is to study how poisoning attacks may be applied to models for detecting bots on Twitter. The results show that by introducing mislabeled data- points into a such a models training data, attackers can reduce its accuracy by up to twenty percent. The possibility of more effective poisoning techniques exists, and remains a topic for future research.

Keywords: Decision Support Systems, Human – Computer Interaction, Virtual and Augmented Reality
Abstract: US Army C5ISR Center Cyber Security Service Provider (CSSP) is a 24/7 Defensive Cyber Operations (DCO) organization that defends US Department of Defense and US Army networks from hostile cyber activity, as well as develops technologies and capabilities for use by DCO operators within the DoD. In recent years, C5ISR Center CSSP has been researching various advanced data visualization concepts and strategies to enhance the speed and efficiency of cybersecurity analyst’s workflow. To achieve these goals Virtual and Mixed Reality (VR/MR) tools have been employed to investigate, whether these mediums would enable useful remote collaboration of DCO operators and whether stereoscopically perceivable 3D data visualizations would enable DCO operators to gain improved hindsight into their datasets. We’ll be giving overview of the capabilities being developed as aligned to our research and operational requirements, our expected outcomes of using VR/MR in training and operational cyber environments and our planned path to accomplish these goals.

Keywords: Decision Support Systems, Supervisory Control
Abstract: It is crucial to provide operators supports for operation planning in complex process plants. This paper presents an operation searching approach used for synthesis of valve and pump operations to establish specific pipe routes. Instead of modeling pipeline fragments, a functional modeling methodology called Multilevel Flow Modeling (MFM) is adopted to represent plant knowledge. Relying on causality feature of MFM, one can identify required state changes of specific functions in the model, and accordingly operations. Since relationships between functions, components, and operations are independent to the modeling object, these generic principles can be implemented into a rule-based reasoning system for inferring operational conditions of pipe routes, namely, operations. An example in the literature has been used to demonstrate the presented approach and application of the software.

Keywords: Decision Support Systems, Human Machine Systems, Display Design
Abstract: The paper presents a dynamic risk-based framework for operational task analysis at nuclear power plants. The dynamic operational risk assessment system is focused on the system configuration management and human error identification. The potential risk sources could be identified using a reliability/risk-based mapping scheme. The risks arising from system functional impairment and/or human errors are mapped out by the noteworthy difference between the curved timeline of average plant risk and point-in-time risk profile. The system modeling process is augmented with structural-functional perspective for facilitating Living Probabilistic Risk Assessment (Living PSA) update, analysis and management. The dynamic operational risk assessment framework forms a well-founded basis for development of Living PSA program and applications.

Keywords: Decision Support Systems, Human – Computer Interaction, Autonomous Systems
Abstract: Mass casualty incidents are a challenging safety-critical domain with high complexity, where management and decision-making can make the difference between life and death. As current paper-based tools and software solutions have severe limitations, we examine the use of integrated computer-based support systems in this domain. Instead of being primarily focused on the replacement of paper-based tools, we are looking for additional value of computer systems to support the incident commanders by providing appropriate presentations and offering assistance functions with different automation approaches. For this purpose, we are developing modular software components that can be combined to an integrated system for the management of casualties. We use a user-centered design approach. Therefore, the needs and abilities of prospective users were analyzed thoroughly and considered in system design. This includes interviews and field observations as well as formative evaluations of prototypes. This article summarizes our research for such an integrated management system for MCIs.

Keywords: Human Machine Systems, Model-Based Design, Human – Computer Interaction
Abstract: The design of load-critical human-machine systems presumes thorough modelling and analysis of load profiles the systems are meant to withstand at peaks loads. The need for modelling is often ignored due to significant modelling effort. We propose an algorithm for learning a subclass of Uppaal timed automata models from interaction logs between system and its environment. The learning method relies on synchronous communication assumption that is specific to interaction of distributed system components. The method is demonstrated on the learning example of IEEE1394 distributed leader election protocol to generate a tester model for HMS testing.

Keywords: Assistive Technology and Rehabilitation Engineering, Adaptive Aiding, Usability Engineering
Abstract: The usage of robots for the rehabilitation training has the potential to relieve the therapists and to enable patients to train independently. However, the intelligent control of robots in direct interaction with humans is still subject to current research activities. This work presents an assist-as-needed control strategy for an industrial lightweight robot during upper arm rehabilitation training. To include the expert knowledge of therapists, a fuzzy logic control strategy for the stiffness of the underlying impedance controller is designed and implemented. Apart from the patient's current status, defined by pain and exhaustion, his or her general capabilities and personal characteristics are also considered by the presented approach. In order to test the designed algorithm, an experiment with an additional weight to simulate a patient suffering paresis and an oval movement in the xz-plane is done. The results illustrate that the controller is able to adapt the robot's behavior according to the current status of the patient and its general condition.

Keywords: Human Machine Systems, Usability Engineering, Assistive Technology and Rehabilitation Engineering
Abstract: Industry 4.0 introduced a large number of new machines and systems to the market. These have been developed with regard to the manufacturing industry, but can also be used to implement applications in other industries. Healthcare is under pressure from demographic change and an increasing staff shortage. New concepts have to be tested and realized to combat this problem. This paper describes the idea of transferring a human-machine collaborative industrial robot to the healthcare sector. An application is set up in an experimental Full-Scope simulation environment and trial experiments are carried out with probands. As preparation for a large-scale experiment, a pretest takes place, with a simple measurement method for situation awareness. The aim is to continuously improve the quality of the set-up and to explore the applicability of survey instruments and to get to know their advantages, in addition to achieving initial results.

Keywords: Assistive Technology and Rehabilitation Engineering, Human – Computer Interaction
Abstract: Neuromuscular strength training of the leg extensor muscles plays an important role in the rehabilitation and prevention of age and wealth related diseases. In this paper, we focus on the design and implementation of a Cartesian admittance control scheme for isotonic training, i.e. leg extension and flexion against a predefined weight. For preliminary testing and validation of the designed algorithm an experimental research and development platform consisting of an industrial robot and a force plate mounted at its end-effector has been used. Linear, diagonal and arbitrary two-dimensional motion trajectories with different weights for the leg extension and flexion part are applied. The proposed algorithm is easily adaptable to trajectories consisting of arbitrary six-dimensional poses and allows the implementation of individualized trajectories.

Keywords: Model-Based Design, Human – Computer Interaction, Human Machine Systems
Abstract: We have developed a scrub nurse robot (SNR) system which is capable of helping surgery. We used C++ programming language to control the mechanical and sensory parts of SNR (peripheral nervous system), but selected UPPAAL to create a comprehensive surgery model (central nervous system) because surgical operations are very complex. To solve a problem that C++ and UPPAAL could not communicate with each other, we developed a software interface “DTRON”. We verified that DTRON realized the communication. Furthermore, by comparison of simulated ‘voluntary’ and ‘reflex’ movements, the delay due to the ‘voluntary’ movements was found imperceptible and negligible because the signal transmission through DTRON took about 0.6 msec.

Keywords: Human Machine Systems
Abstract: Human modelling approaches are typically limited to feedback-only, compensatory tracking tasks. Advances in system identification techniques allow us to consider more realistic tasks that involve feedforward and even precognitive control. In this paper we study the human development of a feedforward control response while learning to accurately follow a ramp-shaped target signal in the presence of a disturbance acting on the controlled element. An experiment was conducted in which two groups of eight subjects each tracked ramps of different steepnesses in a random or ordered fashion. In addition, ordered runs were followed by a ‘surprise’ run with a random ramp steepness. Results show that operators learn rapidly, continue to learn during the entire experiment, and can adapt very quickly to surprise situations. Experiments involving learning operators are challenging, as it is difficult to balance-out all experimental conditions and control for inevitable differences between (groups of) subjects.

Keywords: Virtual and Augmented Reality, Human Machine Systems, Usability Engineering
Abstract: Human workers remain a crucial part of production environments for conducting manual assembly or maintenance procedures, despite increasing automation. These procedures cannot be automated due to small lot sizes and high product variability. Performing manual procedures requires the application of procedural knowledge and motor skills, such as bimanual coordination and complex hand movements. Many training systems for manual procedures have been proposed. However, these systems focus on declarative knowledge about the sequence of work steps. The inherent haptic characteristics and sense for correct tool and component application gets lost. This paper proposes a training system that introduces haptic components for the training of assembly procedures. The proposed training system instructs the user in mounting two physical components by employing haptic and visual interaction. Augmentations and real-time feedback assist the user during the training and enable the assessment of applying accurate torque on screw connections. An evaluation compared the training system against video-based instructions and indicated advantages for the proposed system in terms of objective measures (time on task, precision) and in terms of subjective measures such as usability.

Keywords: Human Machine Systems, Human – Computer Interaction
Abstract: In long-duration space missions, skill retention and generalizability become ever more important as mission length increases, for it is through these capabilities that astronaut crews achieve autonomy. A framework is proposed for future research based on three types of training commonly found in operational scenarios: initial training, refresher training, and just-in-time training. One of the key points of interest identified from the body of literature is the need for a structured and quantifiable approach to characterize skill decay. Such an approach would allow gaining a deeper understanding of the mechanisms through which skill decay operates. Because simulators are used extensively in all types of training, the effects of simulator fidelity on skill retention and generalizability are paramount to understand. Some common fidelity types are defined and their effects on the human operator are discussed. From a review of the literature on all these topics, several research gaps were identified that led to three key opportunities for future research: (1) developing skill decay functions provides theoretical insights into skill decay and allows for several practical applications, such as planning refresher training, (2) the effects of simulator fidelity on skill decay functions should be investigated empirically, and (3)investigating the generalizability of skills learned in initial training to other tasks could provide space crews with greater autonomy. A cybernetic approach might give new insights.

Keywords: Human Machine Systems, Human – Computer Interaction
Abstract: This paper presents the results of an experiment that was performed at NASA Ames Research Center using 18 participants in two different groups who trained a task for ten days, with the goal of identifying how skill generalization would occur between two similar tasks of varying difficulty. A cybernetic approach was used. The first group was trained in a simple one-dimensional tracking task and transferred to a difficult two-dimensional tracking task. For the second group, this was reversed. Training with a simple task before transferring to the difficult task resulted in a slower convergence to final performance. However, it did allow participants to start with a better initial performance in the difficult task. Furthermore, after training with a simple task, participants controlled with a higher gain and generated lower lead time constants. However, possibly due to the number of participants, this experiment did not find any statistical evidence to support the conclusion that training with a simple task version helps in learning a more complex task.

Keywords: Human Machine Systems, Adaptive Aiding
Abstract: This paper addresses the question of online fatigue monitoring in high constrained work environments, by dealing more specifically with the activity of submariners. A state of the science is proposed on the concept of fatigue as well as physiological and behaviour metrics supporting the emergence of a fatigue management system for individuals and teams. From this, a framework for online fatigue monitoring in maritime environments is proposed.

Keywords: Assistive Technology and Rehabilitation Engineering, Decision Support Systems, Usability Engineering
Abstract: Housing services and utilities represent the highest share of the EU Silver Economy. EU Member States are ageing. The demand for specialised housing for older adults and ambient assisted living technologies is expected to triple in the next 40 years. The ageing population is driving the expenditures of health care (HC) and long-term care (LTC) provision without visible improvement in the quality of life of older adults. Ambient assisted living technologies and ambient intelligence can enable residents to live longer in their own homes and in specialised housing in the community while mitigating the increasing public expenditures for health care and long-term care. We present the results of the survey, how older adults in Slovenia perceive the ambient assisted living housing, where large share of older adults, who are already included in municipality home care programs, want to live after a severe decline in their functional capacities. These results enable us to forecast the dynamics of the expected demand for specialised housing for older adults and the expected directions of the development of specialised housing and the supply networks. We present the multiple decrement model to forecast the dynamics of this demand when developing the silver economy. This structure of demand, we show, depends on the demographic and the income of the older adults. Therefore, it influences the probabilities of transitions in a multiple decrement model for forecasting the dynamics of development of specialised housing with embedded AAL technologies for older adults.

Keywords: Human Machine Systems, Assistive Technology and Rehabilitation Engineering, Social Computing
Abstract: Electromyography (EMG) signals can be used for action classification. Nonetheless, due to their nonlinear and time-varying properties, it is difficult to classify the EMG signals and it is critical to use appropriate algorithms for EMG feature extraction and classification. In previous studies various ML methods have been applied. In this paper, we extract four time-domain features of the EMG signals and use a generative graphical model, Deep Belief Network (DBN), to classify the EMG signals. A DBN is a fast, greedy deep learning algorithm that can find a set of optimal weights rapidly, even in deep networks with many hidden layers and a large number of parameters. To evaluate this model, we acquired EMG signals, extracted their features, and then utilized the DBN model as human action classifiers. The real data analysis results are presented to show the effectiveness of the proposed deep learning technique for 4-class recognition of human actions based on the measured EMG signals. The proposed DBN model has potential to be applied in design of EMG-based user interfaces.

Keywords: Virtual and Augmented Reality, Human – Computer Interaction
Abstract: In this paper we report on a study conducted with a group of older adults in which they engaged in participatory design workshops for creating a VR ATM training simulation. Based on observation, recordings and the developed VR solution we present the results of the workshops and offer guidance on best practices concerning organizing opportunities for end users, in this case older adults, to directly engage in co-creation of cutting-edge ICT solutions. These include co-designing interfaces and interaction schemes for emerging technologies like VR and AR. We discuss such aspects as user engagement and hardware and software tools suitable for participatory prototyping. Finally, we present ideas for further research in the area of VR participatory prototyping with users of various proficiency levels, taking steps towards developing a unified framework for co-design in AR and VR.

Keywords: Human Machine Systems
Abstract: In the analysis of human motor skills, tracking tasks with multisine target signals are often performed as they allow for quantitative measurement, identification, and modeling of human control dynamics. In this paper, the same "cybernetic" approach is taken to analyze eye movement dynamics in gaze tracking tasks, where participants had to track a moving target marker across the screen) with their eyes (i.e., a eye-only task) and 2) with their dominant hand (i.e., a eye-hand task). A human-in-the-loop experiment with 10 participants was performed to measure the eye movement dynamics. These two different conditions were performed with four different bandwidths of the multisine target signal driving the movement of the visual stimulus. The results show that the measured eye movement dynamics can be identified from the data of all experiment conditions and can be accurately modeled as an underdamped mass-spring-damper system with a time delay. Furthermore, with increased target signal bandwidth the bandwidth of participants' eye movements also increases. Future development of gaze tracking tasks into a new tool for assessment of altered gaze behavior due to neurological diseases should take the balance between saccadic and smooth pursuit eye movements into account and avoid very high (i.e., too difficult) bandwidths to warrant accurate modelling of gaze dynamics.