Keywords: Assistive Technology and Rehabilitation Engineering, Human – Computer Interaction, Decision Support Systems
Abstract: Freezing of the upper limb in Parkinson's disease patients occurring during writing tests constitutes the research subject of the present paper. Digitisation of the writing and drawing tests coupled with artificial intelligence techniques have demonstrated accurate results in supporting the diagnostics of Parkinson's disease. In the digital setting, the analysis of freezing episodes did not get much attention. The main goal of the present paper is to determine if the neighbourhood of the point where freezing occurred possesses sufficient discriminating power to distinguish between the Parkinson's disease patients and healthy control individuals. For each freezing episode, time intervals of one second before and after are considered. These intervals are described by the hand movement's kinematic and pressure parameters. These parameters are used as features for the standard machine learning workflow that applies a nested cross-validation loop. The paper's main findings have demonstrated that analysis of the freezing neighbourhoods allows distinguishing Parkinson's disease patients from age matched healthy controls. The best results were achieved based on the movements occurring one second after the freezing episode. Kinematic and pressure-based features describing these movements have allowed training classifiers whose accuracy, precision, and recall have reached the values of 0.86, 0.86 and 0.93, respectively. Furthermore, the achieved results are comparable to those available in the literature.

Keywords: Human Machine Systems, Assistive Technology and Rehabilitation Engineering
Abstract: This paper investigates the potential of using a manual pursuit tracking task for quantifying loss of motor skills due to Parkinson's disease (PD), by applying human controller (HC) modeling techniques. With this approach, it is possible to obtain detailed quantitative data on motor performance in terms of control gain, response delay, stiffness and damping. Pursuit tracking data was collected from seven early-stage PD patients and a matched control group at the Erasmus MC in Rotterdam. Tracking performance was significantly worse in the PD group compared to the controls. Furthermore, the PD patients showed significantly lower control gains and degraded neuromuscular damping and bandwidth, which indicates that early-stage PD is associated with loss of quick and fast arm movements. While the PD patients showed less consistent and linear control behavior in the task, their data could still be modeled at high accuracy. Using HC models to quantify PD patients' fine motor skill abilities may contribute to improved (early) detection of motor skill loss in PD, as well as detailed monitoring of symptom development and intervention effectiveness.

Keywords: Human Machine Systems, Assistive Technology and Rehabilitation Engineering
Abstract: Parkinson's disease (PD) is a progressive nervous system disorder that affects movement. PD has a severely negative impact on the quality of life of patients and their caregivers. The timing of treatment depends, amongst others, on the quantification of patients' motor performance. To date, the resolution used in scaling motor performance is too low to detect subtle behavioral changes over time. This paper investigates if ‘longitudinal’ data-sets of motor performance data obtained from tracking tasks can detect behavioural changes in motor performance data representative for PD symptoms. Such longitudinal data were approximated using a combined data-set based on 50 trials of collected experiment data from 25 healthy participants (age range 55-75 years), augmented with 25 bootstrapped samples scaled to represent ‘Mild’ or ‘Severe’ motor performance degradation. An approach based on general linear regression models was tested for its capacity to detect the adverse trends in typical tracking task metrics. Overall, it was found that with this approach in at least 50% of all participants, a simulated change in motor behaviour was successfully detected, a number that may increase to 97% for the most sensitive metric (neuromuscular damping ratio) and consistent participant data. This indicates that the developed approach is promising towards the development of more objective and detailed monitoring of disease progression and treatments in PD patients.

Keywords: Decision Support Systems, Assistive Technology and Rehabilitation Engineering, Cognitive System Engineering
Abstract: Growing research interest has arisen towards automated neurodegenerative disease diagnostics based on the information extracted from the digital drawing tests. Since the performance of modern modelling techniques (machine learning, deep learning) relies heavily on the size of training data available, data scarcity is one of the most significant problems in computer-aided diagnostics. This paper proposes using Generative Adversarial Networks to synthesise digital drawing tests acquired from Parkinson's patients and healthy controls. Four different architectures (StyleGAN2-ADA, StyleGAN2-ADA + LeCam, StyleGAN3 and ProjectedGAN) are evaluated and compared with the traditional data augmentation methods. Convolutional neural networks are utilised for Parkinson's disease diagnostics. Our results indicate that GAN-generated images' addition outperforms the standard augmentation methods in classifying Parkinson's disease in some settings. Therefore, the proposed framework could serve as a potential decision support tool for clinicians in computer-aided fine-motor analysis for neurodegenerative disease diagnostics.

Keywords: Human-Computer Interaction, Human-Machine Interfaces, Machine Learning
Abstract: Digitization, Industry 4.0 and the Industrial Internet of Things, and now also the use of machine learning and artificial intelligence are leading to immense changes in the working environment for plant operators and maintenance technicians. This article highlights the development over the last decades, the current requirements and relevant research topics.

Keywords: Model-Based Design
Abstract: Manufacturing companies worldwide have recently experienced challenging times due to a lack of staff, materials, and components. This has mainly been caused by abrupted logistics chains and collateral effects of the last pandemic situation. Ideally, resilience engineering systems, systems that have recovery capacity from difficulties, are prepared to overcome changes in demand and disruption in production. However, lack of flexibility, adaptability, and available digital data limit the implementation of resilience systems. To overcome this problem with a high number of interrelations considering human-machine interactions, a methodology including Discrete-Event Simulation, Work Domain Analysis, and Functional Resonance Analysis Method is proposed to design, analyze, and improve complex manufacturing systems. These tools allow deeper analysis of the interrelations of the system at different abstraction levels and both with quantitative and qualitative perspectives. Going through an industrial case study, the aim is to increase the capacity and resilience of a leisure-boat manufacturing company producing highly-customized large-size products, which adds additional constraints to the problem. The objectives are to increase flexibility and productivity at the same time as maintaining high-quality product standards. The results highlight the identification of some constraints of the system such as the main production bottleneck, lack of space, and a limited number of transports, molds, and skilled personnel. The implementation and results of the methodology have proved to serve as a decision-support tool, providing insight about limitations of the system to managers and stakeholders, as well as a guideline for increasing capacity and resilience of the manufacturing process.

Keywords: Human Machine Systems, Cognitive System Engineering, Human – Computer Interaction
Abstract: The advancement of automation technologies in the manufacturing industry has transformed human work into monitoring complex processes and intervening as needed. These operations necessitate the ability to adapt to variabilities that arise during the process. This ability concerns interactions between operators, automated machines, and the environment that constitutes socio-technical systems becoming more complex as technology advances. However, the process of demonstrating adaptive skills is unknown because the operators have acquired those skills as tacit knowledge. In this paper, we investigate the experts' adaptive skills to cope with multitasking focusing on the monitoring feature using the Functional Resonance Analysis Method (FRAM). First, we formulate a hypothesis based on the attention allocation characteristics during multitasking, using eye-tracking experiments and interviews. Next, we examine the hypothesis by incorporating the attention characteristics into FRAM models and conducting a simulation study that envisions behavior change caused by the encounter of multitasking. Compared with previous studies on approaches to tacit knowledge, this research's novel point is to analyze experts' features concerning interactions emerging with the environment using the function-based modeling method. The results show that the expert's attention features represented by the FRAM model structure are essential to the adaptive skill to manage variabilities in the working environment. Our research will contribute to elucidating the process of demonstrating adaptive skills in the manufacturing industry.

Keywords: Human Machine Systems, Autonomous Systems
Abstract: Without intervention the widespread adoption of autonomous vehicles could be compromised by an increased incidence of motion sickness compared to conventional cars. To investigate whether passengers’ motion sickness can be reduced by manipulating an autonomous vehicle’s accelerations on a fixed route without altering the travel time, a human-out-of-the-loop experiment was performed in the SIMONA Research Simulator at Delft University of Technology. The experiment consisted of two different driving conditions, in which an identical 22-km road including 52 curves was travelled in 30 minutes. Condition 1 comprised larger longitudinal, but smaller lateral, acceleration values compared to Condition 2. Experimental results suggested that Condition 1 resulted in more severe motion sickness than Condition 2, with fitted learning curves providing final MIsery SCale scores of 1.19 vs. 0.80. A similar relative difference between the two conditions had been predicted by the 6-DOF Subjective Vertical Conflict model. Hence, this model has the potential to, once further developed, support the design of autonomous vehicles by reducing the need to perform costly, time-consuming experiments.

Keywords: Human Machine Systems
Abstract: Twenty-three participants took 10-min solo Urban Air Mobility quadrotor flights as passengers on two separate days in a six-dof large-motion simulator. One flight was in a rotor speed (i.e., RPM) controlled model; the other was under rotor blade pitch (i.e., collective) control. Both were flown in the same mod-eled turbulence. When ranked across test conditions, the severity of participants’ self-reported simulator sickness symptoms paralleled acceleration-derived predictions of motion sickness likelihood in the fol-lowing worst-to-best order: 1) RPM control; 2) collective control; and 3) preflight while still on the vertiport pad. Various objective measures revealed potential impacts of flight roughness on the learning of a visuo-manual reaction-time task and on heart and breathing rate indicators of preflight/inflight pas-senger stress.

Keywords: Human Machine Systems, Usability Engineering
Abstract: This paper aims at identifying the expectations of train drivers or other railway operator staff about Human-Machine Systems (HMS) in future cabins. The identification of the best technical solution needs surveying preferences and efficiencies of possible new information technology configurations of Human-Machine Interfaces (HMI) considering human factors as input and output sensors. Technical recommendations about the train cabin of the future are provided. They consider results from a state-of-the-art on HMI in transport systems, from technology maturity issues, and from two large scale surveys realized during project. Recommendations are then proposed to train manufacturers for deeper investigation or for innovative driving cabin implementation.

Keywords: Virtual and Augmented Reality, Human Machine Systems
Abstract: The Flight Simulation Facilities (FSF) at the National Aeronautics and Space Administration’s (NASA) Langley Research Center (LaRC) provide high-fidelity, man-in-the-loop (MITL) and hardware-in-the-loop (HITL) simulation services to a number of research customers including NASA, academia, aviation industry partners, and other government agencies such as the Federal Aviation Administration (FAA) and the National Transportation Safety Board (NTSB). The Simulation Development and Analysis Branch (SDAB) management has determined that future research related to the emerging Advanced Air Mobility (AAM) sector will require a new type of simulator with a vastly different cockpit layout and field of view requirements for the out-the-window visual systems than any of the current cockpits. This new simulator might also benefit from integrated head-mounted displays (HMD). For these reasons, SDAB has decided to design and build a new cockpit that will meet the future AAM electric Vertical Take-Off and Landing (eVTOL) simulator needs. In this paper, the authors discuss the novel use of state-of-the-art head worn devices (HWD) to present augmented reality (AR) and virtual reality (VR) environments to researchers to improve the quality of the cockpit design and reduce the time-to-market and cost to obtain the new cockpit.