Development Of Human-machine Interface Research Articles

What Do You Need? Information Requirements and Task Analysis of (Future) Advanced Air Mobility Pilots in the Emergency Medical Service

In the domain of Advanced Air Mobility (AAM), Simplified Vehicle Operations (SVOs) promise a reduction in handling complexity and training time for pilots. Designing a usable human–machine interface (HMI) for pilots of SVO-enabled aircraft requires a deep understanding of task and user requirements. This paper describes the results of two user research methods to gather these requirements. First, a traditional Helicopter Emergency Medical Service (HEMS) mission was examined using a Hierarchical Task Analysis (HTA). The findings were used to formulate a theoretical HTA for a single-piloted electric Vertical Take-Off and Landing (eVTOL) system in such a scenario. In the second step, qualitative interviews with seven subject matter experts (pilots and paramedic support) in HEMS operations produced vital user requirements for HMI development. Key findings emphasize the necessity of a simplified information presentation and collision avoidance support in the HMI.

Triboelectric Contact Localization Electronics: A Systematic Review.

The growing demand from the extended reality and wearable electronics market has led to an increased focus on the development of flexible human-machine interfaces (HMI). These interfaces require efficient user input acquisition modules that can realize touch operation, handwriting input, and motion sensing functions. In this paper, we present a systematic review of triboelectric-based contact localization electronics (TCLE) which play a crucial role in enabling the lightweight and long-endurance designs of flexible HMI. We begin by summarizing the mainstream working principles utilized in the design of TCLE, highlighting their respective strengths and weaknesses. Additionally, we discuss the implementation methods of TCLE in realizing advanced functions such as sliding motion detection, handwriting trajectory detection, and artificial intelligence-based user recognition. Furthermore, we review recent works on the applications of TCLE in HMI devices, which provide valuable insights for guiding the design of application scene-specified TCLE devices. Overall, this review aims to contribute to the advancement and understanding of TCLE, facilitating the development of next-generation HMI for various applications.

Local eye-net: An attention based deep learning architecture for localization of eyes

Development of human machine interface has become a necessity for modern day machines to catalyze more autonomy and more efficiency. Gaze driven human intervention is an effective and convenient option for creating an interface to alleviate human errors. Facial landmark detection is very crucial for designing a robust gaze detection system. Regression based methods capacitate good spatial localization of the landmarks corresponding to different parts of the faces. But there are still scope of improvements which have been addressed by incorporating attention. In this paper, we have proposed a deep coarse-to-fine architecture called LocalEyenet for localization of only the eye regions that can be trained end-to-end. The model architecture, build on stacked hourglass backbone, learns the self-attention in feature maps which aids in preserving global as well as local spatial dependencies in face image. We have incorporated deep layer aggregation in each hourglass to minimize the loss of attention over the depth of architecture. Our model shows good generalization ability in cross-dataset evaluation and in real-time localization of eyes.

Extracting Individual Muscle Drive and Activity From High-Density Surface Electromyography Signals Based on the Center of Gravity of Motor Unit.

Neural interfacing has played an essential role in advancing our understanding of fundamental movement neurophysiology and the development of human-machine interface. However, direct neural interfaces from brain and nerve recording are currently limited in clinical areas for their invasiveness and high selectivity. Here, we applied the surface electromyogram (EMG) in studying the neural control of movement and proposed a new non-invasive way of extracting neural drive to individual muscles. Sixteen subjects performed isometric contractions to complete six hand tasks. High-density surface EMG signals (256 channels in total) recorded from the forearm muscles were decomposed into motor unit firing trains. The location of each decomposed motor unit was represented by its center of gravity and was put into clustering for distinct muscle regions. All the motor units in the same cluster served as a muscle-specific motor pool from which individual muscle drive could be extracted directly. Moreover, we cross-validated the self-clustered muscle regions by magnetic resonance imaging (MRI) recorded from the subjects' forearms. All motor units that fall within the MRI region are considered correctly clustered. We achieved a clustering accuracy of 95.72% ± 4.01% for all subjects. We provided a new framework for collecting experimental muscle-specific drives and generalized the way of surface electrode placement without prior knowledge of the targeting muscle architecture.

Recent Advances in Hydrogel‐Based Self‐Powered Artificial Skins for Human–Machine Interfaces

With the rapid development of artificial intelligence, human‐machine interfaces (HMI) are continuing to affect human lifestyles. Artificial skin is a new type of HMI sensor that enables a seamless connection between human and electronic devices. Currently, artificial skin is mostly prepared from rigid materials, which lack flexibility and scalability, thus impeding the development of HMI. Hydrogel, consisting of 3D polymer network and water is similar to human tissues and is therefore an excellent candidate for artificial skin in HMI. The conventional HMI of hydrogel‐based artificial skin includes touch pad and machine control based on capacitive or resistive sensors. However, the energy supply of HMI depends on battery which requires frequent recharging and replacement. Therefore, hydrogel‐based self‐powered artificial skin is expected to become primary interaction medium for the forthcoming generation of HMI. This article reviews the development of hydrogel‐based self‐powered artificial skin for HMI. Various power supply mechanisms of hydrogel‐based artificial skin are discussed. The materials for self‐powered artificial skin are introduced, including ionic hydrogel, ionic‐liquid hydrogel, metal‐based hydrogel, carbon‐based and MXene‐based hydrogel, and conductive polymer‐based hydrogel. The application of the hydrogel‐based self‐powered artificial skin in HMI is also reviewed. Finally, the challenges and development trends in HMI are outlined.

Supporting user experience of shared automated mobility on-demand through novel virtual infrastructure: Making the case for virtual stops

Sustainable and connected mobility concepts for the future are increasingly sought-after. Among many factors, providing high service quality is key to their commercial success. In pick-up scenarios of shared automated mobility on-demand (SAMOD), the new concept of virtual stops (vStop) improves control and planning, and the human-machine interface (HMI) associated with it aims to increase usability for users. This paper elaborates on the new concept in-depth and presents two user-centered online-interview studies. The objectives were 1) capturing users’ information requirements when using SAMOD, and 2) evaluating an early-stage vStop HMI prototype in terms of user experience. Results show that the HMI prototype's high pragmatic quality corresponds to the user's desire for distinctive information. This aspect becomes especially valuable to users when picked up at unfamiliar traffic locations. Conclusions from both studies lead to initial design guidelines for a vStop HMI, which could be implemented using mobile augmented reality. This work is a starting point for user-centered HMI development for vStops. It contributes to widespread deployment of SAMOD in the future and fosters the change towards more sustainable mobility solutions.

Handwritten Digits Recognition From sEMG: Electrodes Location and Feature Selection

Despite hand gesture recognition is a widely investigated field, the design of myoelectric architectures for detecting finer motor task, like the handwriting, is less studied. However, writing tasks involving cognitive loads represent an important aspect toward the generalization of myoelectric-based human-machine interfaces (HMI), and also for many rehabilitative tasks. In this study, the handwriting recognition of the ten digits was faced under the myoelectric control perspective, considering the probes setup and the feature extraction step. Time and frequency domain features were extracted from surface electromyography (sEMG) signals of 11 subjects who wrote the ten digits following a standardized template and 8 sEMG probes were equally distributed between forearm and wrist. Feature class separability was investigated and an aggregated feature set was built to train pattern recognition architectures, i.e. linear discriminant analysis (LDA) and quadratic support vector machine (QSVM). Also, four reduced probes setups were investigated. LDA and QSVM showed mean accuracy of about 97%, with all the forearm and wrist sEMG information. A significant reduction of performances was observed considering the wrist or the forearm only ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leq 92$ </tex-math></inline-formula> %) and when LDA and QSVM were trained with two electrodes information ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\leq 90$ </tex-math></inline-formula> %). For the reliable classification performances in a motor task involving high cognitive demands, like the handwriting, it is required the use of probes fully covering forearm and wrist. Outcomes support the methodological transfer from myoelectric hand gesture to the handwriting recognition, which represents a key aspect in the development of new HMI for rehabilitation tasks.

Fault Diagnosis of Induction Motor Using D-Q Simplified Model and Parity Equations

Induction motors are the horsepower in the industrial environment, and among them, 3-phase induction motors (3PIMs) stand out for their robustness and standard 3-phase power supply. In the literature, there are many approaches to diagnose faults for the nonlinear 3PIM model, and the vast majority focus on a single motor fault, although others address more faults but at the cost of greater computational complexity. In this sense, one of the methods with less computational load and early detection is the parity equation approach, which is based on analyzing the discrepancy between the input and output signals of a real process and a linear mathematical model to generate a residual signal, which contains important information about the fault and is obtained through a suitable selection of a weighting matrix W to isolate the faults as much as possible. The problem in this case study is that the 3PIM model is a nonlinear system. In this work, the fault detection method based on the parity equations approach applied in the 3PIM is explored using a simplified and proposed model of the 3PIM working in the D-Q synchronous reference frame, which is matched with the direct current motor model to guarantee both the existence of the parity space and to ensure a large set of detectable faults in the 3PIM parameters. Simulation and experimental results validate the proposed scheme and confirm a very simple set of residual equations to guarantee both early detection and a large set of detectable faults in: Stator and rotor resistances, stator and rotor inductances, as well as current, voltage, and speed sensors. Additionally, development of human machine interface (HMI) is implemented to validate the proposed scheme.

Innovative HMI and Control Concept for Efficient Thermal Management of Electric Vehicles

This study presents a practical design and implementation of a Thermal Management (TM) concept for Battery-Electric Vehicles (BEVs) including a novel Human-Machine Interface (HMI). The developed system uses a combination of convective air conditioning and radiation heating to cool down and heat up the passengers efficiently. The proposed contribution can be subdivided into four parts: 1) description of the Heating, Ventilation and Air Conditioning (HVAC) system; 2) development of a novel HMI; 3) derivation of an appropriate operating strategy concept; 4) validation of the entire TM system based on a user study using a demonstrator vehicle. The results show that the developed TM concept can substantially support the user to operate the HVAC system more efficiently. A user study was conducted to evaluate passenger comfort and usability. The study revealed that users tend to operate a conventional input panel of an HVAC system inefficiently in some cases. They could achieve better task performance with the new HMI proposed in this study. However, the acceptance of the new concept was rated lower for the new system, as users were already accustomed to the conventional input panel design.

REAL TIME AGE AND GENDER CLASSIFIER

Gender is still a prominent element of our personalities. It also plays an important role in our social lives. Artificial intelligence age forecasts have applications in a variety of disciplines, including smart human-machine interface development, health, cosmetics, and electronic commerce, among others. The ability to estimate people's sex and age from their face photographs is a current and active research topic. The researchers proposed a number of solutions to the problem, but the criteria and actual results are still insufficient. This paper proposes a statistical pattern recognition strategy to solve this challenge. In the proposed method, a Deep Learning technique called Convolutional Neural Network (ConvNet / CNN) is employed to extract features. CNN takes input images and assigns a value to distinct characteristics / elements of the image (learnable weights and biases) and can distinguish between them. Other classification techniques require far more pre-processing than ConvNet. While the filters are created by hand using simple methods, ConvNets can be trained to learn these filters and features. Face photos of individuals were trained with convolutional neural networks in this study, and age and sex were accurately predicted with a high rate of success. There are around 20,000 photos with age and gender. Poses, facial expressions, lighting, occlusion, and resolution are all represented in the photos.

Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

Entering the 5G and internet of things (IoT) era, human–machine interfaces (HMIs) capable of providing humans with more intuitive interaction with the digitalized world have experienced a flourishing development in the past few years. Although the advanced sensing techniques based on complementary metal-oxide-semiconductor (CMOS) or microelectromechanical system (MEMS) solutions, e.g., camera, microphone, inertial measurement unit (IMU), etc., and flexible solutions, e.g., stretchable conductor, optical fiber, etc., have been widely utilized as sensing components for wearable/non-wearable HMIs development, the relatively high-power consumption of these sensors remains a concern, especially for wearable/portable scenarios. Recent progress on triboelectric nanogenerator (TENG) self-powered sensors provides a new possibility for realizing low-power/self-sustainable HMIs by directly converting biomechanical energies into valuable sensory information. Leveraging the advantages of wide material choices and diversified structural design, TENGs have been successfully developed into various forms of HMIs, including glove, glasses, touchpad, exoskeleton, electronic skin, etc., for sundry applications, e.g., collaborative operation, personal healthcare, robot perception, smart home, etc. With the evolving artificial intelligence (AI) and haptic feedback technologies, more advanced HMIs could be realized towards intelligent and immersive human–machine interactions. Hence, in this review, we systematically introduce the current TENG HMIs in the aspects of different application scenarios, i.e., wearable, robot-related and smart home, and prospective future development enabled by the AI/haptic-feedback technology. Discussion on implementing self-sustainable/zero-power/passive HMIs in this 5G/IoT era and our perspectives are also provided.

Tactile Sensor Array With Tertiary Leaf-Vein Structures and Position-Encoded Capacity

Flexible tactile sensor is a preferred alternative in the development of human-machine interface (HMI) and robotics, which directly influences how we understand and interact with physical world. High density and high sensitivity tactile sensor array are urgently desired to assist people acquire tactile information of multi objects. However, although the existing researches exhibit great perception of tactile signals with different magnitudes and frequencies, they also expose limitation in the retrieval of relative position relationships. Herein, we report a wearable and high density (96 sensors on the fingertip) tactile sensor array based on tertiary leaf-vein inspired piezoresistive film. Particularly, a tertiary microstructure in the direction of height, including mid-vein-mimicking wrinkle (MVW), lateral-vein-mimicking wrinkles (LVW) and minor-vein-mimicking wrinkles (mVW), has been demonstrated an effective method to fabricate rough surface. The highly surface-controllable piezoresistive film dramatically enhances the durability (over 5000 cycles) and improves the sensitivity from 8.44 kPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> to 25.80 kPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> under 2 kPa. Accordingly, we propose an innovative position-encoded algorithm aimed at high density tactile sensor array, which has been demonstrated an excellent encode capacity of translating the multi-location tactile information caused by Braille characters into human-readable outputs. This study not only provides a map scanning method for Braille recognition, but also has significant implications for position-based information exchange or tactile encryption.

Development of Human Machine Interface (HMI) Training Kit as A Learning Media for Industrial Automation Engineering Practical Courses

Learning media is one of the learning tools that is needed so that the learning process can run smoothly and students can have competencies in accordance with the expected competency goals. This paper discusses: (1) developing a Human Machine Interface (HMI) training kit as a learning medium in the Industrial Automation Engineering Practical Course; (2) the performance of the HMI training kit; (3) the feasibility of the HMI training kit based on the assessment of media experts, material experts and student responses as training kit users. In developing this training kit using the ADDIE model which consists of Analysis, Design, Development, Implementation and Evaluation. Data collection was carried out by testing the performance of the media, and giving a questionnaire to media experts to assess the feasibility of the media aspect, a material expert questionnaire to assess the appropriateness of the material and a questionnaire to determine student responses as users. The data were analyzed by means of quantitative descriptive analysis which were then categorized. The result of this development is HMI practice learning media for industrial automation engineering practice courses which consist of HMI practice hardware, and jobsheets. The performance of the HMI training kit for input and output parameters, visualization of engine control in the form of buttons, sliders, output visualization, indicator lights, and overall graphics have a 100% correct performance. Based on media experts, design aspects, technical aspects and usefulness aspects were rated very well, so that overall the media aspect received an assessment in the very proper category Based on material experts, technical aspects and aspects of usefullness received an assessment in the very good category, so that overall the material aspect received an assessment in the very proper category. Based on assesment of students as users, the average score student response falls into the very good category.

Desarrollo de un Modelo 3D para un Sistema Infotainment

En la actualidad somos testigos del constante avance en el desarrollo de tecnologías para la industria automotriz donde cada día hay avances y mejoras significativas para brindar mayor comodidad a los pasajeros. Una de las tecnologías del vehículo que está especialmente en desarrollo es el diseño de interfaces de usuario específicas para sistemas infotainment y diferentes tipos de interacción, por ejemplo, la implementación de interfaces de usuario 3D para proporcionar retroalimentación al usuario. Estas interfaces se pueden crear a partir de tecnologías disponibles en el mercado, tanto de software libre como privado para el diseño de modelos 3D y para el desarrollo de Interfaces Humano Máquina utilizados en la industria automotriz. Este artículo describe el proceso que se llevó a cabo para el desarrollo de una interfaz funcional a partir de tecnologías para el modelado 3D y diseño de interfaces. Dicha interfaz proporciona una retroalimentación visual al usuario que permite comprender lo que sucede en el vehículo.

Real-World Use of Partially Automated Driving Systems and Driver Impressions

Background: Automated driving systems (ADSs) have the potential to fundamentally transform transportation by reducing crashes, congestion, and cost while improving traffic efficiency and access to mobility for the transportation-challenged population (US DOT, 2020). However, a recent on-road test of five vehicles capable of SAE Level 2 (SAE, 2016) driving automation equipped with forward collision warning (FCW), adaptive cruise control (ACC), lane departure warning (LDW), and lane keep assist (LKA) revealed that ADSs may not work as expected in typical driving situations, such as approaching stopped vehicles and negotiating hills and curves (IIHS, 2018). Even worse, people may not use ADSs as intended due to their misunderstanding of, over-trust, or distrust in such systems’ capabilities and limitations (IIHS, 2019). As Level 2 ADSs have become commercially available, accounts of unintended uses of these systems and fatal consequences have emerged. For example, a recent news article reported a Tesla driver napping behind the wheel (Fox News, 2019). Objective: Given the growing availability of ADSs on public roadways as well as the risk of their unintended use and safety consequences, this work aimed to better understand (1) realworld use of ADSs, (2) prevalence of unintended use of such systems, and (3) driver impressions after prolonged use of such systems. Method: The research team investigated an existing naturalistic driving database collected from the Virginia Connected Corridor Level 2 Naturalistic Driving Study (VCC L2 NDS, Dunn, Dingus, &amp; Soccolich, 2019). The dataset contains data from 50 participants who drove personally owned vehicles for 12 months. Participating vehicles were equipped with longitudinal control systems (e.g., ACC) at the minimum, although most also had lateral control systems (e.g., LKA). Specifically, we investigated safety-critical events (SCEs, Guo &amp; Fang, 2013) of different severity levels (e.g., crashes and near-crashes) captured in the data. We also examined drivers’ responses to a post-study questionnaire that captured drivers’ subjective ratings on the usefulness and usability of the ADS. Results: We found that 47 out of 235 (20%) SCEs involved ADS use. An in-depth analysis of 47 SCEs revealed that people misused ADSs in 57% of SCEs (e.g., engaged in secondary tasks, used the systems not on highways, or with hands off the wheel). During 13% of SCEs, the systems neither reacted to the situation nor warned the driver. A post-study survey showed that people found ADSs useful and usable. However, the more participants were positive to ADS features, the more they felt comfortable engaging in secondary tasks, which is an unintended side effect of Level 2 ADSs as they require the human driver’s supervision. This study also captured some scenarios where the ADSs did not meet driver expectations. Many people reported that the longitudinal control features did not respond well to cutting-in leads (23% of participants) and stopped leads (14%). The lateral control features were often automatically disengaged when encountering blurred lane markings (14% of participants reported) and had difficulties when negotiating curves (9%). Conclusion: This study contributes to a better understanding of the capabilities and limitations of early production SAE L2 vehicles, the prevalence of the unintended use of ADSs, and drivers’ perceptions of these new technologies. Designers of human-machine interfaces (HMIs) for such systems should always consider the possibility of drivers’ overconfidence in the systems. Therefore, it might be better for vehicles to have multimodal HMIs (Large et al., 2019) adaptive to not only the urgency of situations but also driver state by monitoring driver behavior and engagement in the primary task of driving. Application: The findings from this study may inform the development of HMIs, training programs, and owner’s manuals to reduce the unintended use of ADSs and safety consequences. The identified characteristics of situations where the ADSs failed to warn drivers during SCEs will further inform the development of testing scenarios to ensure ADS safety.

Ultraelastic Yarns from Curcumin-Assisted ELD toward Wearable Human-Machine Interface Textiles.

Intelligent human–machine interfaces (HMIs) integrated wearable electronics are essential to promote the Internet of Things (IoT). Herein, a curcumin‐assisted electroless deposition technology is developed for the first time to achieve stretchable strain sensing yarns (SSSYs) with high conductivity (0.2 Ω cm−1) and ultralight weight (1.5 mg cm−1). The isotropically deposited structural yarns can bear high uniaxial elongation (>>1100%) and still retain low resistivity after 5000 continuous stretching–releasing cycles under 50% strain. Apart from the high flexibility enabled by helical loaded structure, a precise strain sensing function can be facilitated under external forces with metal‐coated conductive layers. Based on the mechanics analysis, the strain sensing responses are scaled with the dependences on structural variables and show good agreements with the experimental results. The application of interfacial enhanced yarns as wearable logic HMIs to remotely control the robotic hand and manipulate the color switching of light on the basis of gesture recognition is demonstrated. It is hoped that the SSSYs strategy can shed an extra light in future HMIs development and incoming IoT and artificial intelligence technologies.

BIM-Based Human Machine Interface (HMI) Framework for Energy Management

This study proposes a Building Information Modeling (BIM)-based Human Machine Interface (HMI) framework for intuitive space-based energy management. The BIM-based HMI supports building managers with a method of linking data between BIM and Building Energy Management System (BEMS), which are heterogeneous systems, and provides space-based real-time energy monitoring. This study also proposes a BIM and BEMS data linking framework for systematic BIM-based HMI development. Towards this end, the BIM-based HMI framework was defined after deriving the considerations and requirements necessary for linking the energy control point and BIM through a questionnaire designed by practitioners. Through case analysis, the authors implemented BIM-based HMI and analyzed its effects. The results of the analysis confirmed the positive effects (3.9/5.0) on the connectivity of BIM-based HMI, the benefits (4.3/5.0) for real-time data monitoring, the system function expandability, and the BIM-based spatial intuitiveness.

Wearable Ultrasound-Based Decoding of Simultaneous Wrist/Hand Kinematics

The advancement in artificial hands challenges the development of human-machine interface. In this study, we present a wearable ultrasound-based interface for decoding simultaneous and proportional (SP) wrist/hand kinematics. Different from traditional velocity-based SP control, this study focuses on position-based SP control that aligns with human proprioception. Results show that simultaneous wrist rotation and hand closing can be accurately decoded from ultrasound, albeit using a contralateral training scheme. Comparative analysis shows that the performance of ultrasound is significantly better than the prevailing surface electromyography. Moreover, it has potential to predict simultaneous multi-degree-of-freedom (DoF) wrist/hand kinematics using single-DoF training by a linearly enhanced training strategy. This is the first study to achieve SP position control via ultrasound, paving the way for the intuitive interaction with future prosthetic devices.

Test-Retest Reliability of Time-Domain EEG Features to Assess Cognitive Load Using a Wireless Dry-Electrode System.

Human Machine Interfaces (HMIs) can provide critical support and improve daily task functionality for prosthesis users or social interaction for patients with locked-in syndrome using an assistive communication device. One goal in the development of sophisticated HMIs is to reduce the cognitive load (CL) they place on the user to promote the use of the technology. Electroencephalogram (EEG)-derived measures collected with wired wet-electrode systems have been used to assess CL in laboratory environments and have demonstrated acceptable test-retest reliability. Assessment of CL during real-world unconstrained HMI operation, however, requires the use of a wireless dry-electrode EEG system which provides easier electrode application and untethered movement. However, the test-retest reliability of wireless dry-electrode systems to quantify CL has not been explored. Ensuring the consistent capture of CL-related signals across multiple sessions is critical if these devices are to be used to assess how improvements in HMIs affect CL. Therefore, the current study used a wireless dry-electrode EEG system to compare Evoked Response Potential (ERP) features of a simple auditory oddball task to measure CL during two separate testing sessions a week apart. ERPs of 11 subjects were recorded while participants performed a virtual task at two difficulty levels. A significant correlation was found between the P300 component of the ERPs and subjective ratings of CL during both testing sessions. Furthermore, there was a statistically significant test-retest reliability for this same ERP feature and similar signal-to-noise ratios (SNRs) across sessions.Clinical Relevance- This is an initial step in validating wireless dry-electrode EEG systems to assess cognitive load across multiple sessions. The evidence presented is critical if dry-wireless EEG systems are to be used to identify aspects of HMIs that reduce CL in clinical and real-life environments. Assessing CL in unconstrained environments can better inform clinicians and technology developers in their design of future HMIs.

DEVELOPMENT OF THE HUMAN-MACHINE INTERFACE OF THE DIRECTIONAL DRILLING SIMULATOR

Background The human-machine interface (HMI) with the control of technological equipment operation for directional drilling covers engineering solutions that ensure the interaction of human operators of various professions (driller, directional drilling engineer, drilling supervisor) with the drilling equipment they manage. Nevertheless, this HMI should also be developed taking into account the possibility of solving more complex problems of multi-level control of the directional drilling trajectory, including using artificial intelligence methods. The directional drilling simulator HMI has differences for these operators, who are actively involved in the technological process of directional drilling control during their joint training: the HMI for the directional drilling engineer and the supervisor is carried out mainly through dialog windows on the computer display, and the HMI with the drilling equipment for the driller is provided by handles, buttons, switches, etc., as well as various displays, including digital ones, for displaying technological information. The HMI for a directional drilling control simulator for operators, including a driller, can be built exclusively through dialog windows (software simulator) without significantly reducing the quality of training. The quality of directional drilling simulator HMI is estimated during the training with this simulator for the joint training of the main operators involved in the directional drilling control, so the relevant performance criteria are calculated. Simulators for oil and gas industry spesialists have various purposes: simulators can be developed with the aim of training specialists, demonstrating the technological process, as well as simulation. However, whatever the simulator is, it is required to simulate the production process as best as possible and more realistic to simulate the working environment. Due to the large number of downhole telemetry systems manufacturers, their various modifications, the directional drilling simulator HMI cannot exactly match all the HMI elements of the telemetry systems software. Nevertheless, the authors set themselves the goal of developing the most versatile directional drilling simulator HMI, which would be close to the of most telemetry systems software HMI. Aims and Objectives The aim of the research is the HMI development for the software simulator for oilg - to develop requirements for the simulator HMI; - to develop and optimize the functional parts of the dialog windows of the directional drilling simulator; - to evaluate the effectiveness of the directional drilling simulator HMI Results The HMI of the directional drilling simulator was developed, which is capable of realistically simulating the most complete list of the main technological processes when drilling directional and horizontal wells. The HMI dialog windows reflect the operation of the HMI field software for supporting oil and gas drilling. The simulator HMI has shown its effectiveness during the trainings with this simulator for the joint training of a driller, an directional drilling engineer and a drilling supervisor.