Abstract - Human behavior, decision-making, and general well-being are significantly influenced by emotions. Emotion recognition has become crucial for creating user-centered and adaptive systems as artificial intelligence's influence grows. Traditional methods like sentiment analysis of voice, text, or facial expressions are frequently constrained by cultural bias, background noise, or deliberate masking, which renders them unreliable for real-time applications. Because physiological signal-based techniques, especially electroencephalography (EEG), directly record emotion-related brain activity, they have become viable substitutes for these difficulties. The EXG Pill, a small, non-invasive bio-signal acquisition device that may be used on a daily basis, is used in this work to propose an AI-powered human–machine interface for emotion monitoring. The frontal lobe regions (Fp1, Fp2), which are closely associated with emotional processing, are where EEG data are obtained. Features are extracted following preprocessing procedures such as amplification, filtering, and noise reduction. Two important indicators are employed: Power Spectral Density (PSD), which measures alertness across frequency bands, and Frontal Alpha Asymmetry (FAA), which indicates emotional valence. To identify emotions like calm, happy, sad, and furious, they are categorized using SVM, CNN, and LSTM models. Emotional states are visualized and adaptive feedback is given via a real-time dashboard. With prospective uses in personalized education, assistive technology, mental health, and emotion-aware interaction, the system exhibits increased accuracy, portability, and usability. Key Words: Emotion recognition, EEG signals, EXG Pill, Frontal Alpha Asymmetry, Power Spectral Density, Human–Machine Interface, Real-time monitoring, Deep learning, Machine learning, Assistive technology, Mental health, Brain–computer interface.

ABSTRACT Pneumatic systems are essential in industrial automation for actuator control. Double acting cylinders are operated by 5 port 3 position valves wherein for two extreme positions, there are mechanical stops. At the intermediate (neutral) valve position, piston rod stopping accuracy and response time are influenced by factors such as rod inertia, air compressibility, valve hysteresis and unequal forces on both the sides of the piston. Industrial applications such as pneumatic press, clamping, palletising require cylinder to stop at an intermediate position of the valve, wherein the stopping accuracy & response time are paramount factors. This study investigates the effect of cylinder orientation, blank end pressure, and rod end pressure on the stopping accuracy and response time of a double-acting single-rod pneumatic cylinder using the Taguchi method. An experimental test rig using PLC (Programmable Logic Controller) & HMI (Human Machine Interface) is developed for the assessment of the best stopping accuracy & response time at each orientation of the cylinder. The best stopping accuracy of 6% is observed with a response time of 1.42 seconds for a vertical cylinder configuration. The L9 orthogonal array and analysis through MinitabR identified cylinder orientation as the most significant factor, followed by blank and rod end pressures. Statistical analysis confirmed the model’s reliability, with residual plots showing normality and low autocorrelation. The findings provide valuable insights for optimising pneumatic cylinder performance, leading to improved precision and responsiveness in automation systems.

This study constructed an integrated underwater pipeline monitoring system, which combines pipeline posture sensing modules and pipeline leakage detection modules. The proposed system can achieve the real-time monitoring of pipeline posture and the comprehensive assessment of pipeline damage. By deploying pipeline posture sensing and leakage detection modules in array configurations along an underwater pipeline, information related to pipeline posture and flow variations is continuously collected. An array of inertial sensor nodes that form the pipeline posture sensing system is used for real-time pipeline posture monitoring. The system measures underwater motion signals and obtains bending and buckling postures using posture algorithms. Pipeline leakage is evaluated using flow and water temperature data from Hall sensors deployed at each node, assessing pipeline health while estimating the location and area of pipeline damage based on the flow values along the nodes. The human–machine interface designed in this study for underwater pipelines supports automated monitoring and alert functions, so as to provide early warnings for pipeline postures and the analysis of damage locations before water supply abnormalities occur in the pipelines. Underwater experiments validated that this system can precisely capture real-time postures and damage locations of pipelines using sensing modules. By taking flow changes at these locations into consideration, the damage area with an error margin was estimated. In the experiments, the damage areas were 8.04 cm2 to 25.96 cm2, the estimated results were close to the actual area trends (R2 = 0.9425), and the area error was within 5.16 cm2 (with an error percentage ranging from −20% to 26%). The findings of this study contribute to the management efficiency of underwater pipelines, enabling more timely maintenance while effectively reducing the risk of water supply interruption due to pipeline damage.

In this study, a flexible hydrogel film sensor based on the intermixing of poly(vinyl alcohol) (PVA) and biomass-derived carbon nanoparticles (CNPs) was prepared and microstructures were constructed by replicating sandpaper templates on its surface. The sensor thus has good overall sensing performance with a sensitivity of 101 kPa−1, a fast response/recovery time of 22 ms and 20,000 fatigue cycles. The sensor was experimentally verified to accurately capture human joint movements, current signals of written letters, and weight differences in the size of spherical objects. Based on this, a breathing phase classification framework was constructed using the 1D-CNN algorithm, achieving a synergistic enhancement effect between environmentally scalable materials and Deep learning algorithms. This approach not only improves the signal discrimination function, but also provides new ideas for wearable medical monitoring, haptic feedback and intelligent robot human–machine interface.

UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency logistics optimization, UAV path planning and scheduling algorithms, collaborative optimization between ground vehicles and UAVs, emergency response decision support systems, low-altitude economy and urban air traffic management, and intelligent transportation system integration. Research findings indicate that UAV delivery technologies in emergency contexts have evolved from single-aircraft applications to intelligent multi-modal collaborative systems, demonstrating significant advantages in medical supply distribution, disaster relief, and search-and-rescue operations. Current technological development exhibits four major trends: hybrid optimization algorithms, multi-UAV cooperation, artificial intelligence enhancement, and real-time adaptation capabilities. However, critical challenges persist, including regulatory framework integration, adverse weather adaptability, cybersecurity protection, human–machine interface design, cost–benefit assessment, and standardization deficiencies. Future research should prioritize distributed decision architectures, robustness optimization, cross-domain collaboration mechanisms, emerging technology integration, and practical application validation. This comprehensive review provides systematic theoretical foundations and practical guidance for emergency management agencies in formulating technology development strategies, enterprises in investment planning, and research institutions in determining research priorities.

The aviation industry prioritizes safety and has embraced innovative approaches for both reactive and proactive safety measures. Machine learning (ML) has emerged as a useful tool for aviation safety. This systematic literature review explores ML applications for safety within the aviation industry over the past 25 years. Through a comprehensive search on Scopus and backward reference searches via Google Scholar, 87 of the most relevant papers were identified. The investigation focused on the application context, ML techniques employed, data sources, and the implications of contextual nuances for safety analysis outcomes. ML techniques have been effective for post-accident analysis, predictive, and real-time incident detection across diverse aviation scenarios. Supervised, unsupervised, and semi-supervised learning methods, including neural networks, decision trees, support vector machines, and deep learning models, have all been applied for analyzing accidents, identifying patterns, and forecasting potential incidents. Notably, data sources such as the Aviation Safety Reporting System (ASRS) and the National Transportation Safety Board (NTSB) datasets were the most used. Transparency, fairness, and bias mitigation emerge as critical factors that shape the credibility and acceptance of ML-based safety research in aviation. The review revealed seven recommended future research directions: (1) interpretable AI; (2) real-time prediction; (3) hybrid models; (4) handling of unbalanced datasets; (5) privacy and data security; (6) human–machine interface for safety professionals; (7) regulatory implications. These directions provide a blueprint for further ML-based aviation safety research. This review underscores the role of ML applications in shaping aviation safety practices, thereby enhancing safety for all stakeholders. It serves as a constructive and cautionary guide for researchers, practitioners, and decision-makers, emphasizing the value of ML when used appropriately to transform aviation safety to be more data-driven and proactive.

Understanding human behavior is essential for designing effective human–machine interaction (HMI) panels, especially those manipulated by the human upper limb. To establish the ergonomic basis of HMI panel layout design, this article presents a theoretical framework for evaluating upper-limb manipulation comfort. Firstly, an anatomical analysis is conducted to identify dominant muscles involved in upper-limb movements. A quantitative index focused on muscle activation intensity is defined to evaluate upper-limb manipulation comfort. The design problem of HMI panel layout is further transformed into finding the most comfortable area for upper-limb manipulation on the expected panel. Furthermore, the proposed comfort assessment and HMI panel design methodologies were validated through experimental studies and questionnaires. Finally, we compared the effect of different number of muscle groups on the evaluation of upper-limb handling comfort through principal component analysis. This article provides inspiration for enriching and developing the ergonomics of human–machine interface panel design.

This article discusses the philosophical-theological implications that derive from scientific evidence in relation to the use of brain–machine interfaces (BMIs). By reflecting on the neuroscientific foundations of human freedom in BMI experiments, the law and ethics scholar Nita Farahany distinguishes between freedom of choice and freedom of action. She argues in favor of the key role played by freedom of action (rather than freedom of choice) to account for legal and moral responsibility. However, despite this, it has been demonstrated that monkeys (similarly to human beings) can use BMIs to perform their desired movements with artificial/robotic arms. I discuss these studies and argue that nonhuman beings can also actualize their intended movements by deliberating through their freedom of action. This strongly suggests that morality is not a uniquely human phenomenon but is also present, although in an “embryonic stage,” in nonhuman creatures. I argue that this can impact the way in which we describe the notion of imago Dei.

The classical five-sense model overlooks key dimensions of human sensory experience, failing to account for internally regulated and bioelectrically resonant modalities. This paper introduces a theoretical Nine-Sense Model that expands conventional frameworks by incorporating four additional sensory circuits: hemoperception (vascular sensing), piloception (electromagnetic hair-field sensitivity), vomeronasal chemodetection, and ultrasonic perception.Grounded in anatomical, neurophysiological, and electrophysiological evidence, these circuits are conceptualized as self-regulating bioelectrical loops operating across somatic and autonomic systems. Rather than being pathological or anomalous, they are presented as latent but functional sensory modules within the general population—supported by findings from sensory plasticity, affective neuroscience, and neuroelectric feedback studies.This model reframes sensation not as passive reception, but as a dynamic resonance process involving phase coherence, bioelectrical entrainment, and nonlinear amplification. Perception is thus reinterpreted as an emergent phenomenon of structured circuit interaction—shifting explanatory paradigms from symbolic representation to systemic physiological resonance.By integrating perspectives from cognitive science, neurophysiology, and biophysical communication, this framework offers a novel account of human sensation as a distributed, recursive, and conscious–nonconscious regulatory system. Its implications extend to neurodiversity, trauma perception, and human–machine interfacing.

The implementation of Modbus and DNP3 (Distributed Network Protocol) over TCP/IP represents a significant advancement in integrating industrial communication standards with modern IoT-based control and cybersecurity frameworks. This study presents a dual-platform experimental implementation of these protocols using the Texas Instruments TMDSCNCD28388D controlCARD and the Arduino Uno, each interfaced with a Simulink-based Human–Machine Interface (HMI). The system architecture enables seamless data exchange between field devices and supervisory applications over Ethernet, supporting real-time monitoring, remote actuation, and secure data acquisition. The TMDSCNCD28388D, equipped with a dual-core C2000 microcontroller and integrated F28388D processor, provides deterministic control for industrial nodes, while the Arduino Uno serves as a low-cost alternative for small-scale IoT testbeds. Both implementations employ Simulink models for system design, simulation, and code generation, ensuring modularity and platform independence. The study emphasizes the integration of industrial automation and IoT protocols within a cybersecurity-aware framework. A layered encryption model was incorporated into TCP/IP communication to evaluate data confidentiality, integrity, and resilience against common cyber threats such as spoofing and denial-of-service attacks. The Simulink HMI acts as both a visualization and command layer, enabling real-time supervisory control and anomaly detection through embedded MATLAB scripts and dashboard logic. Experimental results demonstrate high communication reliability, with Modbus achieving faster request–response cycles under low-load conditions, while DNP3 exhibited greater robustness against packet loss and network interference. The hybrid approach validates the feasibility of deploying standardized SCADA protocols in distributed IoT environments, supporting industrial cyber-physical systems where interoperability and security are critical. This work contributes to the evolving field of cyber-secure industrial automation by demonstrating an end-to-end methodology for implementing Modbus/DNP3 over TCP/IP using embedded microcontrollers and model-based design tools. The outcomes highlight the importance of integrating communication protocols, cybersecurity measures, and model-based engineering to develop resilient, intelligent, and scalable industrial IoT architectures.

This article brings the concept of neuroergonomic workcell with its essential components (psychological and physical assessment, nonphysical, physical, and strategic support) for improving the well-being and productivity of workers at their workplaces. A proof-of-concept neuroergonomic human-centered workstation is demonstrated in a real factory environment for a typical industrial laborious task: assembly. The pilot workstation introduces a fully portable, noninvasive electroencephalogram (EEG)-based users’ mental workload assessment, a nonobtrusive human–machine interface, illustrative graphical assembly guidelines, a collaborative robot assistant, and an intelligent task scheduler. The subjects’ performance and workload were assessed using a NASA Task Load Index questionnaire, three EEG workload indices, hand gesture detection accuracy, the number of errors, and task duration. We identified a notable correlation between multiple EEG indices of workload and NASA score results. The new workstation boosts productivity with better performance and fewer errors on the assembly line while reducing mental demand. Its modular design ensures easy integration and adaptation into factory settings, optimizing manual assembly processes.

As critical infrastructure, hydropower dams require efficient and accurate detection of underwater structural surface defects to ensure their safety. This paper presents the design and implementation of a robotic control system specifically developed for underwater dam inspection in hydropower stations, aiming to enhance the robot’s operational capability under harsh hydraulic conditions. The study includes the hardware design of the control system and the development of a surface human–machine interface unit. At the software level, a modular architecture is adopted to ensure real-time performance and reliability. The solution employs a hierarchical architecture comprising hardware sensing, real-time interaction protocols, and an adaptive controller, and the integrated algorithm combining a fixed-time disturbance observer with adaptive super-twisting controller compensates for complex hydrodynamic forces. To validate the system’s effectiveness, field tests were conducted at the Baihetan Hydropower Station. Experimental results demonstrate that the proposed control system enables stable and precise dam inspection, with standard deviations of multi-degree-of-freedom automatic control below 0.5 and hovering control below 0.1. These findings confirm the system’s feasibility and superiority in performing high-precision, high-stability inspection tasks in complex underwater environments of real hydropower dams. The developed system provides reliable technical support for intelligent underwater dam inspection and holds significant practical value for improving the safety and maintenance of major hydraulic infrastructure.

Haptic feedback is an underutilized modality in traditional human-steering environments and has the potential to enhance the overall user experience. This paper introduces the application of a mathematical design method as a guiding approach for intelligent automobile interaction interface design. Based on the proposed design method, the usability and potential of a novel tangible human–machine interface for maintaining situational awareness in conditional autonomous driving mode are discussed. The device offers haptic feedback of operational information about approaching vehicles, aiding drivers in preparing for takeover control. A user study employing behavioral and eye-tracking techniques validates the efficacy of haptic feedback in improving passengers' understanding of their driving situation. The findings suggest that haptic feedback can significantly contribute to maintaining awareness of surrounding traffic conditions during autonomous driving.

Simulators are an effective tool for improving tram driver training. In urban rail transportation, the fidelity of reproducing the driver’s working environment is crucial due to the high diversity of vehicle models. This study presents a structured assessment model for evaluating the mapping of a tram driver’s console in a universal simulator. The model is based on expert judgment and utilizes fuzzy logic to evaluate four key criteria: perspective, button placement, functionality, and time required to locate safety buttons. A group of 30 experts, including experienced tram drivers and technical specialists, assessed the fidelity of the simulated consoles for three tram types: Solaris Tramino S105p, Moderus Gamma LF 06 AC, and Škoda 16T RK. The results enable the classification of console fidelity levels (low, moderate, high) and support the identification of design inconsistencies. The proposed model provides a standardized tool for assessing simulator realism, which can be applied by transport operators, manufacturers, and training centers to improve simulator configurations. Researchers may also use the model as a methodological framework for further evaluation studies involving human–machine interface fidelity.

This study presents an automated industrial wastewater treatment system based on Siemens programmable logic controller (PLC) that optimizes reagent dosing, aeration, sedimentation, and sludge separation. The system uses accurate pH sensors, dosing pumps, solenoid valves, and a human–machine interface (HMI), and real-time monitoring is provided by a Teltonika TRB255 communication module (<45 sec. response time). As a result, the treatment cycle time was reduced by 31%, reagent consumption by 30%, and operator intervention was reduced from 95 to less than 15 min per day, achieving a pollutant removal efficiency of 89%. A two-layer LSTM architecture developed on the PyTorch platform predicts pH (6.7–7.7), temperature (12–20 °C), and reagent consumption (~9.8 kg/cycle). The model was trained with 240 h of data (64 neurons, learning rate 0.001). The validation loss remained stable, indicating reliable learning. The study confirms that AI-based automation provides greater process stability, meets environmental standards, and promotes sustainable resource use. The scientific novelty of this study is the application of an advanced long short-term memory (LSTM) model to predict wastewater treatment process parameters, allowing for accurate prediction of pH, temperature, flow, and reagent consumption, etc. This provides an opportunity to optimize the process and reduce costs, while ensuring high treatment efficiency and stability. Although there are several publications on the application of artificial intelligence models in the field of industrial wastewater treatment, this is a relatively new field, and there are little data in the scientific literature.

ObjectiveTo evaluate the feasibility of electromyography (EMG)-based human–machine interfaces (HMIs) for high-demand activities such as driving based on performance, cognitive workload, usability, and safety measures.BackgroundUpper-limb amputees face challenges in performing everyday tasks, including driving. EMG-based HMIs offer potential solutions, particularly for wrist disarticulated and trans-radial amputee, but their effectiveness in complex tasks like driving requires further investigation.MethodNineteen able-bodied participants completed a driving simulation study using an EMG-based HMI, dominant hand, and both hands. Participants performed various driving maneuvers including straight lane driving, overtaking, and 90-degree turns at intersections. Driver performance, cognitive workload (measured by blink rate and subjective measures), usability (USE questionnaire), and safety were assessed.ResultsUsing the EMG-based HMI led to higher lane offset and steering angle compared to conventional methods, but demonstrated lower steering entropy in some situations. Cognitive workload was higher for EMG-based HMI, while usability scores were lower. Safety measures were mixed, with EMG-based HMI showing better performance at intersections but lower lane offset and steering angle safety scores overall.ConclusionThe study highlights both limitations and opportunities presented by EMG-based HMIs in high-demand tasks such as driving. While the system exhibited lower performance in some conditions, it demonstrated potential for controlled driving, particularly during specific maneuvers. The higher cognitive workload and lower usability scores indicate areas for improvement.ApplicationThe findings provide valuable insights for the development of more effective EMG-based HMIs, supporting future research and clinical trials aimed at enhancing mobility and independence for individuals with upper-limb amputations.

Learning a novel motor task involves searching within the joint space to form new movement coordination patterns that achieve the task goal. This search process is characterized by systematic changes in joint angle coordination over time, requiring variability in coordination patterns. Motor learning studies have often highlighted the benefits of practice variability on task performance and have primarily focused on search processes at the task level, neglecting the underlying joint level. This study aims to identify differences due to imposed task variability in search behavior within both the task space and the joint space. Participants were divided into two groups based on their practice schedule (blocked vs. random) and performed a lateral interception task using a novel body–machine interface paradigm with redundant mapping between movement signals and paddle position. The results showed that participants successfully learned the required movement coordination in both practice groups. However, random practice led to increased search behavior at both the task and joint levels. Furthermore, analysis of the search structure revealed that covariation in coordination patterns was higher with random practice. Introducing variability during practice did not affect task performance but significantly influenced the amount and structure of search behavior.

• We present an interface for displaying sensor data for vehicle remote assistance. • A user study investigated the augmented interface under fog and higher workload. • The interface reduced collisions and improved situation awareness and usability. • The interface may accelerate the safe deployment of highly automated vehicles. Highly automated vehicles (SAE level 4) occasionally require human support. A remote assistant can help the vehicle from a distance. To date, virtually all human–machine interface (HMI) concepts for remote operation rely on cameras on board the vehicle for assessing its environment. However, in adverse weather, a remote assistant may not be able to decide based on video streams only. We propose an HMI concept for augmenting video streams with visualized data from vehicle sensors. In an experiment, 34 participants assisted the driving automation in a left-turn task using the augmented HMI concept. Visibility was varied by inducing fog. Results show that the augmented HMI effectively supported participants in their remote assistance task. Particularly when foggy, the augmentation reduced collisions, improved situation awareness, and received higher usability ratings. The results imply that augmentation is effective for increasing safety, especially in poor-visibility environments. Future research should examine implications for workplace design.

This study proposes a multi-objective optimization scheduling method for edible oil transportation in smart manufacturing, focusing on centralized control and addressing challenges such as complex pipelines and shared resource constraints. The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. Additionally, the transport time is calculated using the Hagen–Poiseuille law by considering the viscosity coefficients of different oil types. To minimize both cost and time, task execution sequences are optimized based on a Pareto front approach. A 3D digital model of the pipeline system was developed using C#, SolidWorks Professional, and the Helix Toolkit V2.24.0 to simulate a realistic production environment. This model is integrated with a 3D visual human–machine interface(HMI) that displays the status of each task before execution and provides real-time scheduling adjustment and decision-making support. Experimental results show that the proposed method improves scheduling efficiency by over 43% across various scenarios, significantly enhancing overall pipeline transport performance. The proposed method is applicable to pipeline scheduling and transportation management in digital factories, contributing to improved operational efficiency and system integration.

The human–machine system (HMS) and human–machine interface (HMI) are among the top factors that affect the development of advanced systems, equipment, and products [...]