ABSTRACT Light, adaptable, and distributed power sources are essential for materializing various wearable devices and popularizing Internet‐of‐Things (IoT) applications. While triboelectric nanogenerators (TENGs) represent a promising solution of wearable energy, many existing fabric‐based TENGs (f‐TENGs) face challenges in terms of weight, environmental adaptability, and scalable manufacturing. Here, we report a unitary, waterproof, and industrially compatible f‐TENG that efficiently harvests energy from diverse natural and biomechanical sources, including rain, wind, and human motion, while functioning as a self‐powered sensor and human–machine interface. The f‐TENG incorporates sueding‐treated polyethylene and nylon fabrics with spray‐coated silicone rubber particles to enhance charge transfer, alongside a porous polyurethane spacer that optimizes compressibility and contact–separation efficiency. This design reduces device weight by over 8 times compared to previous systems while achieving higher electrical output (315 V open‐circuit voltage and 118 mW/m 2 power density). Critically, all fabrication processes align with standard industrial textile manufacturing, ensuring scalability and cost‐effectiveness. We demonstrate applications in health monitoring, speech recognition, interactive controls, and sports training, providing a new direction for fabricating lightweight and cost‐effective multifunctional TENGs, and highlighting the potential of the f‐TENG to enable future generations of self‐powered e‐textiles and sustainable wearable systems.

This article presents a haptic feedback system combining a flexible electromagnetic actuator with off‐the‐shelf components and virtual/augmented reality (VR/AR) platform to interact with the skin. The system translates targeted VR signals into localized, real‐time vibrations on the forearm. Existing actuator technologies struggle to balance flexibility, scalability, and control over displacement and resonance frequency ranges, limiting their suitability for wearable systems. Moreover, research‐oriented devices are highly specialized and costly, making them difficult to reproduce at a large scale. To address these challenges, we propose an actuator design framework with a tunable model that enables control over displacement and resonance frequency. Using this model, we develop a scalable actuator (12 × 12 × 3.6 mm 3 ) in a 6 × 4 array, leveraging commercial coils, mounted on a wearable sleeve. The device delivers displacements up to 15.8 μm at a resonance frequency of 220 Hz, aligning with the sensitivity of Pacinian corpuscles for high‐frequency vibrotactile feedback. To validate its performance, we implement a VR/AR case study using a Meta Quest 2 system to simulate a haptic laser pointer named “Haptix World” . Our key contributions include: (i) tractable actuator design model, (ii) high‐displacement flexible electromagnetic actuator, and (iii) complete human–machine interface pipeline that bridges VR interactions with physical haptics.

Introduction. Restorative neurosurgery and machine interface.

Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardware, high-density and flexible electrodes, and embedded acquisition modules combined with modern signal processing and machine learning have significantly enhanced the robustness and applicability of EMG-based systems. This review provides an integrated overview of EMG generation, acquisition standards, and preprocessing techniques, including adaptive filtering, wavelet denoising, and empirical mode decomposition. Feature extraction methods across the time, frequency, time–frequency, and nonlinear domains are compared with respect to computational efficiency and suitability for real-time systems. The review synthesizes classical and contemporary pattern-recognition approaches, from statistical classifiers to deep architectures such as CNNs, RNNs, hybrid CNN–RNN models, transformer-based networks, and graph neural networks. Key challenges, including signal non-stationarity, electrode displacement, muscle fatigue, and poor cross-user or cross-session generalization, are examined alongside emerging strategies such as transfer learning, domain adaptation, and multimodal fusion with IMU or FMG signals. Finally, the paper surveys rapidly growing EMG applications in prosthetics, rehabilitation robotics, human–machine interfaces, clinical diagnostics, and sports analytics. The review highlights ongoing limitations and outlines future pathways toward robust, adaptive, and deployable EMG-driven intelligent systems.

As the Human Machine Interface evolves, AI is steadily reshaping the labor market by eliminating low-skill tasks while generating demand for high-skill, technology-driven jobs. This article examines both the benefits and drawbacks of AI for employment. Automation is already displacing jobs in manufacturing, logistics, retail, and customer service, creating pressure on untrained laborers while intensifying demand for highly skilled workers. Many displaced employees struggle to transition into new roles, and without effective support, they risk relying on shrinking welfare systems. Wage suppression may temporarily mask these issues but offers no long-term solution. Although automation fosters new opportunities in fields such as data science, machine learning, cybersecurity, and renewable energy, these roles are largely inaccessible to unskilled workers. Developing countries face particular challenges, requiring robust reskilling programs to mitigate job dislocation. The article highlights the urgent need for education systems to integrate AI literacy, promote cross-disciplinary learning, and emphasize ethical training to prepare future labor forces. Governments must adopt proactive policies that facilitate the shift toward high-skilled employment. By reforming education, encouraging continuous learning, and fostering collaboration between public and private sectors, societies can manage the risks of automation while leveraging AI for sustainable innovation and inclusive growth.

Level 2 driving automation requires continuous driver supervision, yet common attention metrics often capture gaze allocation rather than the structure of supervisory scanning. This study proposes a quantitative approach for describing supervisory gaze organisation using first-order Markov chain analysis of gaze transitions. Forty-three licensed drivers (N=43) completed a simulator drive with Level 2 automation for either 5 or 15 min (between-subjects), representing typical Japanese expressway intervals between service areas. Supervisory behaviour was analysed at the scenario level, without introducing secondary tasks, allowing attentional drift to emerge naturally under automation. Eye-tracking data were manually annotated frame-by-frame at 60 Hz and modelled as transition probability matrices across key Areas of Interest (AOIs): road centre, mirrors, periphery, and the human–machine interface. Compared with the 5 min condition, the 15 min condition showed fewer mirror-to-road-centre recovery transitions and slower System-Recognised Reaction Time (SRRT) at the takeover request. These patterns suggest a gradual weakening of supervisory gaze organisation rather than a simple loss of attention. The proposed framework offers a reproducible way to calibrate driver monitoring and evaluate human–machine interfaces by linking gaze transition probabilities to takeover readiness. By quantifying how supervisory behaviour reorganises under extended automation in realistic driving scenarios, this study provides a practical basis for the development of safety-relevant driver monitoring indicators in Level 2 driver assistance systems.

The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In the context of Industry 4.0, the ability to monitor and control industrial processes in real time is paramount. The present paper designs and implements a system for monitoring and controlling an industrial assembly line based on mixed reality. The technology employed to facilitate communication between the system and the industrial line is S7.Net. These elements facilitate direct communication with the industrial process equipment. The system facilitates the visualization of operating parameters and the status of the equipment utilized in the industrial process and its control. All data is superimposed on the physical environment through virtual operational panels. The system functions independently, negating the necessity for intermediate servers or other complex structures. The system’s operation is predicted on a series of algorithms. These instruments facilitate the automated analysis of industrial process parameters. These devices are utilized to ascertain the operational dynamics of the industrial line. The experimental results were obtained using a real industrial line. These models are employed to demonstrate the performance of data transmission, the identification of the system’s operating states, and the system’s ability to shut down in the event of operating errors. The proposed system is designed to function in a variety of industrial environments within the paradigm of Industry 4.0, facilitating the utilization of multiple virtual interfaces that enable user interaction with various elements through which the assembly process is monitored and controlled.

ABSTRACT Despite significant advances being made in pressure sensor technologies, driven by increasing demand for wearable devices, future Internet of Things (IoT) applications, and electronic skin (e‐skin), critical challenges persist in achieving high sensitivity, high pressure resolution, rapid response, and a wide linear range. Here, we report a cost‐effective and easy‐to‐fabricate pressure sensor that simultaneously achieves high sensitivity and an extensive linear operating range by emulating the multi‐modulus structure of human skin. Typically, these two properties are inversely related, rendering their simultaneous optimization highly challenging. Our sensor design employs a porous structure, composed of two layers of distinct moduli; this is achieved by precisely adjusting the base to crosslinker ratio of polydimethylsiloxane mixed with multi‐walled carbon nanotubes (MWCNTs). The synergistic effect of the MWCNTs and porous structure results in a high sensitivity (2.24 kPa − 1 ), while the dual‐modulus configuration extends the linear response (up to 45 kPa). Moreover, the sensor demonstrates excellent reproducibility and can maintain a stable response even after 6000 cycles of mechanical deformation at 15 kPa. These findings underscore the sensor's efficacy in diverse pressure detection scenarios and its potential for applications in human–machine interface systems and soft robotics.

A growing challenge in mobile robotics is the reliance on complex graphical interfaces and rigid control pipelines, which limit accessibility for non-expert users. This work introduces a latency-aware benchmarking framework that enables natural-language robot navigation by integrating multiple Large Language Models (LLMs) with the Robot Operating System 2 (ROS 2) Navigation 2 (Nav2) stack. The system allows robots to interpret and act upon free-form text instructions, replacing traditional Human–Machine Interfaces (HMIs) with conversational interaction. Using a simulated TurtleBot4 platform in Gazebo Fortress, we benchmarked a diverse set of contemporary LLMs, including GPT-3.5, GPT-4, GPT-5, Claude 3.7, Gemini 2.5, Mistral-7B Instruct, DeepSeek-R1, and LLaMA-3.3-70B, across three local planners, namely Dynamic Window Approach (DWB), Timed Elastic Band (TEB), and Regulated Pure Pursuit (RPP). The framework measures end-to-end response latency, instruction-parsing accuracy, path quality, and task success rate in standardised indoor scenarios. The results show that there are clear trade-offs between latency and accuracy, where smaller models respond quickly but have less spatial reasoning, while larger models have more consistent navigation intent but take longer to respond. The proposed framework is the first reproducible multi-LLM system with multi-planner evaluations within ROS 2, supporting the development of intuitive and latency-efficient natural-language interfaces for robot navigation.

This paper presents a large language model (LLM)-based system for autonomous maintenance in manufacturing facilities. While many machine alarms are interpreted with existing manuals, understqanding and acting on these instructions of all facilities remains a challenge for operators. The proposed system processes user inputs including error codes, identifies corresponding procedures from manuals, and decomposes them into structured action sequences. These sequences include action, user interface target, preconditions, and expected outcomes, and are executed by agent capable of interacting with Human–Machine Interfaces (HMIs). The proposed system is built on an LLM-powered multi-agent framework comprising four agents: a chatbot, solution_finder, actor, and supervisor. Each agent operates based on role-specific prompts that define their responsibilities and decision rules. Instead of relying on predefined rule sets, the system interprets unfamiliar or previously unseen alarms by reasoning over machine manuals and context, enabling flexible and scalable maintenance. The system was implemented on a HMI system of CNC machine tools and successfully performed automatic responses to selected alarms. Prompt-based control ensure adaptability to other machines, and the use of a local LLM maintains data security. This approach enables general-purpose, self-directed maintenance with minimal operator intervention.

This paper proposes an optical three-dimensional (3D) point cloud acquisition and sketching system, which is not limited by the measurement size, unlike traditional 3D object measurement techniques. The system employs an optical displacement sensor for surface displacement scanning and a six-axis inertial sensor (accelerometer and gyroscope) for spatial attitude perception. A microprocessor control unit (MCU) is responsible for acquiring, merging, and calculating data from the sensors, converting it into 3D point clouds. Butterworth filtering and Mahoney complementary filtering are used for sensor signal preprocessing and calculation, respectively. Furthermore, a human–machine interface is designed to visualize the point cloud and display the scanning path and measurement trajectory in real time. Compared to existing works in the literature, this system has a simpler hardware architecture, more efficient algorithms, and better operation, inspection, and observation features. The experimental results show that the maximum measurement error on 2D planes is 4.7% with a root mean square (RMS) error of 2.1%, corresponding to the reference length of 10.3 cm. For 3D objects, the maximum measurement error is 5.3% with the RMS error of 2.4%, corresponding to the reference length of 9.3 cm. Finally, it was verified that this system can also be applied to large-sized 3D objects for outlines.

This study presents the development of a real-time three-phase electrical power monitoring system based on a digital power meter, Programmable Logic Controller (PLC), Human–Machine Interface (HMI), and Internet of Things (IoT) technology. The proposed system was designed to acquire key electrical parameters, including voltage, current, and frequency, and to display the measured data consistently for both local monitoring via an HMI and remote monitoring through IoT-enabled devices. Data acquisition from the power meter was performed using Modbus RTU communication, with the PLC acting as the central data processing and control unit, and the HMI and IoT platforms provided visualisation and remote access. The system implementation and testing were performed under different load conditions to evaluate the functionality, data consistency, and communication reliability. The experimental results show that the measurement values displayed on the HMI and IoT platforms are identical, indicating stable data communication and correct register mapping. Minor differences between the power meter readings and the monitoring system were observed, with a maximum error of 0.6195 %, which was attributed to the display resolution and rounding limitations of the power meter rather than data processing errors. The results demonstrate that the developed system is reliable and accurate for real-time three-phase power-monitoring applications. The integration of industrial devices with IoT technology enhances data accessibility, improves the visibility of electrical system conditions, and supports modern monitoring requirements, making the proposed system suitable for industrial and technical facility applications.

Abstract Amid the Fourth Industrial Revolution, the People’s Liberation Army’s rapid innovation has refocused academic attention on the contours of future warfare. Mainstream rationalist approaches typically analyze China’s military rise by reifying identity and interests and by positing a linear link between technological innovation and national power. From this vantage, the PLA’s growing innovation is seen as increasing systemic war risks through preventive logic, offense–defense imbalance, and expansion of non-kinetic warfare. In contrast, this article explains Xi-era military innovation through the materialist dialectics of Sinicized Marxism. This shows that as new-quality productive forces are converted into new-quality combat capabilities, party-led routines work to internalize self-restraint and rule compliance in the armed forces, reducing information asymmetries at the human–machine interface. The hegemonic reproduction of the party-army is a constitutive practice through which the Chinese Communist Party enhances ontological security, contributing to debates on how military innovation is produced and organized in authoritarian regimes.

Stretchable pressure sensors are essential for intelligent machine perception and human-machine interactions, yet their accuracy is often compromised by deformation, due to limited strain tolerance of continuous-structure sensing materials. To resolve this issue, we propose a multi-level discrete array strategy to fabricate superior deformation-adaptive sensors. Then, an innovative and eco-friendly approach is developed to fabricate micro-nano hierarchical ZnO arrays through combined plasma-enhanced atomic layer deposition and hydrothermal growth. The micro-scale pattern acts as first-level discrete structure, enabling high stretchability of sensor, while ZnO nanorods function as the secondary structure to dissipate interfacial shear stress. This multi-level design ensures 98.2% strain insensitivity even under 100% strain, and the sensor exhibits a 1 Pa detection limit and dynamic monitoring capability during dynamic deformation, validating its potential in morphing electronics. Notably, this approach can create a sensing array with different sensitivities by adjusting the ZnO area ratios. The resulting E-skin serves as a human–machine interface for controlling robotic hand movements, which requires only a single pair of electrodes and doesn’t need external power source.

Myoelectric control paradigms have the potential to enable continuous volitional control of bionic limbs in various movement conditions. Although individuals with below knee amputations and an agonist–antagonist muscle interface (AMI) were proven to display a greater degree of continuous volitional control in bionic ankle-foot systems with respect to conventional socket-suspended prosthetic users, it remains unclear how myoelectric interfaces could translate to non-AMI prosthetic users with bone-anchored prostheses (BAP). This preliminary study proposes a human–machine interface (HMI) based on a neuromechanical model to enable volitional, continuous myoelectric control of a bionic leg in AMI and BAP users, walking across various speeds and ground inclinations. Differently from state of the art solutions, the proposed HMI is based on a digital twin of the intact leg, synthesizing the user’s phantom limb musculoskeletal function as controlled by muscle activations measured from the residuum. When embedded in a real-time framework, it enabled the participants to achieve volitional modulation of prosthesis peak plantar-dorsiflexion torques timing and amplitude during overground walking at three speeds (between 1.6 and 3.96 km/h), with case studies provided during calf-raises (30, 45, and 60 bpm) and ramp ascent walking (3 and 5% incline). Before prosthesis control tests, the participants underwent a 2-day gait training session. Results showed that all three subjects learned how to alter initial muscle activation patterns so that an average of 87% of peak activation timing fell within target ranges. The proposed neuromechanical modeling technology opens new avenues toward generalizable HMIs for the volitional control of active prostheses beyond set conditions and amputation types.

This paper presents the conceptual design and simulation of an automated greenhouse climate control system aimed at melon cultivation in the coastal region of Ecuador. The proposal is based on the use of programmable logic controllers (PLC) and a human–machine interface (HMI), developed entirely within a simulation environment using synthetic data and predefined engineering assumptions. The system is designed to regulate critical environmental variables such as air temperature, relative humidity, and soil moisture through a structured and modular control logic implemented in Ladder language. The adopted methodology prioritizes logical and functional validation of the system without relying on physical sensors or field testing, allowing the evaluation of operational coherence under different simulated environmental scenarios. The results demonstrate stable and consistent system behavior, with appropriate automatic responses to conditions of thermal and water stress. Additionally, the proposed control strategy shows potential improvements in water use efficiency and greenhouse microclimate stability. This study represents a preliminary, non-experimental contribution that provides a structured foundation for future stages involving physical implementation, field validation, and the integration of advanced technologies in agro-industrial greenhouse automation systems.

Motor unit (MU) discharge information extracted via real-time electromyogram (EMG) decomposition shows superiority in dexterous finger motion decoding. However, some critical constraints might limit the performance of multi-degree-of-freedom (DoF) muscle force estimation, including the neglect of the temporal cumulative effects of MU discharges and the rigid MU pool allocation across fingers. A hybrid encoder-decoder framework integrating EMG decomposition with TFR (Temporal Firing Rate)-Net was proposed for multi-DoF force estimation. The initial encoder involved decomposing EMG into MU spike trains and firing rates, which are subsequently processed through TFR-Net for hierarchical encoding and decoding. This framework uses temporal firing rate to model force accumulation instead of a twitch model, and allocates MU weights dynamically and flexibly in multi-fingers and multi-force level tasks instead of rigid MU pool allocation. Two baselines (firing rate-based regression method and twitch force model method) were evaluated on 15-min EMG from 10 subjects performing alternating multi-finger isometric extensions. The results showed the proposed method demonstrated superior comprehensive performance with a higher correlation (R ${}^{\mathbf {{2}}}$ : $0.80~\pm ~0.12$ vs. $0.75~\pm ~0.14$ vs. $0.62~\pm ~0.19$ ) and a lower prediction error (root-mean-square error: 6.32% $\pm ~1.89$ % vs. 7.24% $\pm ~1.82$ % vs. 9.96% $\pm ~2.54$ % maximum voluntary contraction) compared with the two comparison methods, and an improved computational efficiency (Flops: 1.350k vs. 408.093k vs. 0.016k) compared with the twitch force model method. Further development of the proposed method could potentially provide a robust human machine interface for dexterous finger force prediction in realistic applications.

The accelerated urbanization and rapid growth of the global population has resulted in the generation of massive amounts of municipal solid waste, mainly containing plastics. The improper disposal and minimal recycling rate of these waste materials exacerbate several environmental challenges. The present study focuses on the recycling of waste polystyrene (PS) by fabricating triboelectric nanogenerators (TENGs) based on PS films synthesized by various methods, i.e., solution casting, electrospinning, and spray coating as a positive triboelectric material. Fourier-Transform Infrared (FTIR) spectroscopy and Field-Effect Scanning Electron Microscopy (FE-SEM) were used to analyze the functional groups and morphology of the synthesized films. The spray-coated PS-based TENG exhibited the highest electrical performance with a maximum open circuit voltage (Voc) of 690 V at 4 Hz, short-circuit current of (Isc) of 67 µA at 5 Hz, and maximum output power density of 28 W m−2 at 50 MΩ of load resistance. This enhanced performance can be attributed to the nanofibrous nature and increased surface area of the spray-coated films. The practical applications of PS-TENGs were also demonstrated, and include charging various capacitors and powering a calculator using a rectified voltage circuit. Also, the real-time control of a small remote-controlled (RC) car has been successfully demonstrated using PS-based TENGs. This innovative study paves the way for a circular economy by recycling waste plastic material into novel functional materials and accelerates the pathway for energy harvesting and smart sensors.

The Controlled Environment Agriculture (CEA) system controls the soil and climatic conditions to enhance agricultural productivity and resource efficiency in a sustainable way. This work offers a framework of the Monitoring Digital Twin (mDT) of CEA operations optimization, which combines automation, real-time monitoring, and predictive analytics. A SELEC DIGIX-1 Programmable Logic Controller (PLC) has automated control of environmental parameters, whereas the digital twin continually gathers sensor data, extracts features, and stores them to be used in the information analysis. The main objectives of the proposed system are (i) to predict Crop yield based on Crop Yield Prediction Dataset and (ii) to detect (weed, pesticide and plant disease) based on Plant Village Dataset. A Median Filter with Z-Score Normalization is used to perform data preprocessing to improve the quality of data and eliminate noise. Gray Level Co-occurrence Matrix (GLCM) is used to obtain effective feature extraction, and then, the Deep Recurrent Q Network model is used with a Convolutional Neural Network (CNN) to classify them. The model has a better performance of an accuracy of 0.97, precision of 0.96, recall of 0.93, F1-score of 0.93, and Root Mean Square Error (RMSE) of 0.1673, of superior performance compared to conventional methods. The data is processed and stored in DynamoDB and hence accessed by Python-based Edge computing devices on user request. In addition, the SP112-GT40-S-CE Human Machine Interface (HMI) displays the insights (locally and remotely) like predicting crop yields, identifying the weed, pesticide suggestions, and identifying the disease in the plant. On the whole, this mDT framework improves the CEA ecosystem by integrating digital twin technology and deep learning in order to attain intelligent automation, early anomaly and sustainable agricultural productivity.

A hierarchical core-shell tribopositive yarn TENG with electrospun polyethyleneimine (PEI) nanofibers for sustainable energy harvesting and human machine interface application