Search Results (1,015)

Flexible and wearable electronics require soft sensing materials that balance mechanical compliance, stable signal transduction, and durability for human–machine interfaces (HMIs). To address the limitations of single-filler systems, we propose a poly(vinyl alcohol) (PVA)/aramid nanofiber (ANF)/MXene organogel (PAM) as a multifunctional soft platform. This design integrates a PVA physically crosslinked network with ANF for mechanical reinforcement and MXene for electrical functionality. The optimized PAM composite exhibits outstanding mechanical properties, including a fracture stress of 2931 kPa, a fracture strain of 676%, and a fracture toughness of 9.04 MJ m⁻³. Importantly, PAM serves as a single material platform configurable into three sensing modalities. The resistive strain sensor achieves a gauge factor of 3.1 over 10–100% strain and enables the reliable recognition of human joint movements and gestures. The capacitive pressure sensor delivers a sensitivity of 0.298 kPa⁻¹, rapid response/recovery times of 30/10 ms, and is integrated with a wireless module to control a smart car. Furthermore, the PAM-based triboelectric nanogenerator (TENG) delivers excellent electrical outputs (V_oc = 123 V, I_sc = 0.52 μA, Q_sc = 58 nC) and functions as a self-powered smart handwriting pad, achieving a machine-learning-based recognition accuracy of 97.6%. This work demonstrates the immense potential of the PAM organogel for advanced, self-powered HMIs. Full article

Flexible factories and adaptive fabrication lines offer a testbed for advanced multimodal interaction concepts that can support the inclusion of disabled workers in Industry 5.0 manufacturing systems. The study synthesizes interdisciplinary data from ergonomics, industrial automation, and EU regulatory frameworks to establish a conceptual model for human-machine interaction. Building on conceptual modeling and a structured literature analysis, the study proposes a six-step integration framework that links task demands, worker capabilities, and interaction modalities within human-in-the-loop manufacturing environments. Although no empirical case study was conducted in this phase, an exemplary application is presented for a semi-automated bike wheel manufacturing process. Detailed machine-based assembly line flows and simulated process data were utilized for illustrative purposes to depict the process and validate the proposed Capability–Task Matching Matrix. The results operationalize the human-centric vision of Industry 5.0 by providing a structured methodology for the inclusion of disabled workers within fabrication environments. The findings are organized into two primary components: the conceptual development of the Integration Approach and its practical application to a semi-automated industrial use-case. Finally, a particular focus is placed on Brain–Computer Interfaces (BCIs) as an emerging interaction channel that enables non-muscular control, attention monitoring, and neuroadaptive feedback, complementing conventional interfaces rather than replacing them. The framework is illustrated through application to the same semi-automated bicycle wheel assembly line, where BCI-supported interaction, augmented interfaces, and robotic assistance are mapped to specific production tasks and assessed in terms of feasibility and technological maturity. Drawing on the paper’s results, an explanatory 10-year roadmap outlines the feasibility and phased deployment of BCI solutions. It aligns technological advances with European regulations and a vision for a fully inclusive manufacturing enterprise. Full article

Surface electromyography (sEMG) signals are important for assessing muscle activity, neuromuscular behavior, and movement stability. sEMG signals are widely used in athlete performance monitoring and human–machine interface applications. However, existing methods have limitations in classification, accuracy and generalization across users. In this study, a real-world dataset was generated from 30 professional wrestlers using an 8-channel system based on 10 physical movements and technical elements. Nine time-domain and energy features, mean absolute value (MAV), integrated EMG (IEMG), root mean square (RMS), simple square integral (SSI), fourth power (4POW), wavelength (WL), difference absolute standard deviation (DASDV), variance (VAR), and average amplitude change (AAC), were systematically evaluated separately and in combination. Five classifiers were compared: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), and Neural Networks (NNs). The models were evaluated for accuracy, sensitivity, specificity, positive predictive value, and F1-score. The generalization ability was analyzed through cross-subject (24/6) and cross-session validation protocols. The nine feature combinations achieved the highest classification accuracy of 97.8% with the RF algorithm. The proposed approach can serve as a practical basis for real-time muscle activity monitoring, movement classification, and rehabilitation systems. Full article

Precise monitoring of forearm muscle groups is crucial for decoding motor intentions in human–machine interfaces (HMIs) and rehabilitation. However, traditional surface electromyography (sEMG) electrodes face significant challenges in densely packed muscle regions with large skin deformations, leading to severe signal crosstalk and unstable contact. Here, we report a flexible, low-cost 16-channel copper electrode array system designed for the high-density monitoring of multiple forearm muscle activities. Through a facile fabrication process, rigid copper is transformed into a conformable sensing interface. The optimized serpentine interconnects endow the array with excellent stretchability and effectively isolate motion-induced stress, ensuring high-quality signal acquisition under complex deformations. The high-density 2 × 8 array enables the spatiotemporal mapping of distributed flexor and extensor muscle groups. Integrated with a customized wireless data acquisition system, the array successfully demonstrates real-time, multi-channel sEMG monitoring of various hand movements (e.g., fist clenching, wrist flexion/extension), clearly revealing specific muscle activation patterns. This low-cost, high-performance flexible sensor array provides a highly promising tool for complex gesture decoding, electromyographic imaging, and next-generation wearable HMIs. Full article

This study evaluated the resistance under load of a novel monolithic prosthetic design integrating functional orthodontic components within a digitally fabricated framework. Sixty-six specimens were allocated into three groups: (1) a Design Group consisting of one-piece 3D-printed customized metal copings with integrated brackets or tubes; (2) a Porcelain Crown Group with conventionally bonded orthodontic attachments; and (3) a Natural Teeth Group with brackets and tubes bonded to extracted human teeth. Each group included premolar (bracket) and molar (tube) subgroups (n = 11). All specimens were subjected to shear loading using a universal testing machine. Higher resistance values were observed in the monolithic group (92.56 ± 63.88 MPa) (p < 0.001); however, these values represent structural resistance rather than shear bond strength. Despite the wide variability, all measured values remained above the clinically accepted threshold. No statistically significant differences were observed between porcelain crowns and natural teeth in premolar or molar subgroups. The findings indicate that eliminating the adhesive interface enhances structural integrity under shear forces. This monolithic orthodontic–prosthetic approach may provide a clinically relevant alternative in cases where conventional bonding is not feasible and supports a fully digital, patient-specific workflow through scanner library integration. Full article

Compliant mechanisms have contributed to many advances in soft robotics, and there is strong motivation to translate these ideas to assistive devices where adaptive motion at the human interface is required. This work presents novel reconfigurable compliant joints (RCJs) as a parameterized joint element for functional biomimicry in lower-extremity joints for prosthetic knees and ankle–foot orthoses, with concepts that extend to other limb joints. The RCJ uses a rigid hub and outer ring joined by an array of flexible links with centerlines defined by cubic Bézier curves. Link shapes are organized into four Bézier classes (A–D), with base types using 10, 12, or 14 uniformly distributed link slots and variants generated by modifying active-link count and distribution, forming a structured morphology space of 12 configurations for machine design. Dual-extrusion 3D-printed prototypes are characterized by a custom testing apparatus using a 2.2 kN load cell at 25 mm/s over a 0–90° rotation range across six recorded load cycles to measure torque–angle curves and stiffness under large deformations. Angle-dependent stiffness is evaluated over three fixed intervals (0–30°, 30–60°, and 60–90°) to quantify multi-stage behavior. A 2-dimensional corotational frame model and a Simscape Multibody model, including a rolling-contact knee configuration, use the same parameterization to relate geometry, nonlinear mechanics, and system-level motion. Experiments and simulations show multi-stage torque–angle profiles and predictable stiffness modulation across all configurations, with both magnitude and transition angle tunable through Bézier class and active-link distribution, positioning the RCJ as a CAD/CAE-compatible joint architecture for assistive devices or wearable robotic systems and a basis for advancing functional biomimicry in compliant mechanism design. Full article

To address the strong nonlinearity, coupling, and time-delay characteristics in greenhouse environmental regulation, as well as the large overshoot and limited robustness of conventional proportional–integral–derivative (PID) control, while considering the practical constraint that complex intelligent control methods are difficult to deploy directly on low-cost industrial controllers, this study proposes a predictive fuzzy PID control method for greenhouse environments under programmable logic controller (PLC)-based edge deployment. An integrated remote monitoring and control system with a “PLC–human–machine interface (HMI)–cloud–mobile” architecture was also developed. Based on the intelligent greenhouse experimental platform of Yunnan Agricultural University, the proposed method was validated for greenhouse temperature and air humidity regulation through MATLAB simulations, PLC deployment, and on-site operation tests. The results showed that all four control strategies were able to effectively track the setpoints of greenhouse temperature and humidity, while predictive PID and predictive fuzzy PID achieved better overall performance than conventional PID and fuzzy PID. Predictive fuzzy PID performed best in the humidity channel, whereas its performance in the temperature channel was close to that of predictive PID but with more stable disturbance recovery and better overall balance. On-site operation results further showed that, under typical operating conditions, the tracking error of the actual greenhouse temperature relative to the target temperature could be maintained within approximately ±1 °C, while the error of the actual air humidity relative to the target humidity remained within approximately −2% to 3% RH. These results verify the engineering feasibility of the proposed method on resource-constrained industrial PLC platforms. The proposed method can provide a useful reference for the lightweight and intelligent upgrading of small- and medium-sized greenhouse environmental control systems. Full article

In response to the redundant spatio-temporal modeling and insufficient adaptation to dynamic decision-making in eye-tracking interaction of smart home interfaces, a smart home interface eye-tracking response optimization model based on spatio-temporal Transformer and gate control cross-attention is proposed. It adapts the physiological characteristics of eye-tracking jumps through dynamic sparse attention gating to compress computational redundancy and combines multi-objective reinforcement learning attention modulation to construct a closed-loop decision-making mechanism, optimizing interface parameters in real-time. Experiments showed that the model reduced eye-tracking trajectory prediction error by 23.7% compared to advanced benchmarks, increased the success rate of adapting to dynamic mutation scenarios to 89.2%, and controlled performance fluctuations within 2.3% under noise interference. In high-fidelity user testing, the accuracy of cross-task gaze transfer reached 93.4%, the failure rate of glare interference was optimized to 2.4%, and the user cognitive load index was reduced by 27.9%. Its resource consumption and energy consumption were reduced by 26.7% and 44.9%, respectively, while its posture deviation tolerance remained at 3.5°. The sparse spatio-temporal modeling of the spatio-temporal adaptive Transformer module and the enhanced gating mechanism of the hierarchical gated cross-attention module work together to break through the limitations of traditional methods in computational efficiency and dynamic feedback, providing high-precision and low-latency eye-tracking interaction solutions for smart home interface systems, and promoting the practical evolution of personalized human–machine collaborative control. Full article

The development, evolution, and operation of a cyber–physical system are cross-domain, holistic processes. The process encompasses all elements of a cyber–physical system, including computation infrastructure, software, interfaces to the physical world, human interactions, and safety and security engineering. The process is holistic because it must assure conceptual integrity and correct interoperability across all elements of the CPS. Unfortunately, at every stage of this process, vulnerabilities can be introduced into the system (due to negligence, mistakes, lack of skills, malicious activities, etc.). These dormant vulnerabilities can cause failures of the runtime system, possibly resulting in damage, loss of property or life, safety accidents, or security incidents. A promising approach to mitigate such risks is runtime anomaly detection using artificial intelligence/machine learning. This tutorial paper introduces the fundamental concepts of AI/ML anomaly detection and describes the corresponding intervention mechanisms. Automated intervention mechanisms are the last line of defense against failures, faults, malfunctions, and malicious activities—and their unfortunate consequences. The paper remains at the conceptual level and defers implementation details to subsequent publications. The content addresses advanced students (at the master’s level) and researchers entering this fascinating field. Full article

Human–Machine Interfaces (HMIs) are increasingly important in vehicles and other safety-critical systems, yet approaches to their usability and User eXperience (UX) evaluation remain fragmented. This systematic literature review investigates how HMIs are empirically evaluated across domains, with a primary focus on automotive HMIs, complemented by evidence from related safety-critical domains. The review examines UX and usability evaluation methodologies, tools, standards, and technological trends reported in recent research. Peer-reviewed journal articles published between 2015 and 2025 were considered if they addressed empirical usability or UX evaluation of HMIs. Searches were conducted in Scopus and ScienceDirect databases following PRISMA guidelines. From n = 659 records initially identified, n = 82 papers were included in the final analysis. The literature was synthesized using a descriptive and narrative approach, focusing on evaluation contexts, testing methodologies, sensor-based tools, applied standards, and assessment metrics. Most papers investigated automotive HMIs, while fewer addressed aerospace, industrial, maritime, and other safety-critical applications. Simulation-based user testing emerged as the dominant evaluation approach, frequently supported by eye-tracking and physiological sensing technologies and subjective evaluation questionnaires. A more detailed analysis revealed that adherence to international standards (e.g., ISO 9241 and ISO 26262) was not always consistently evident. Overall, the evidence highlights substantial methodological heterogeneity, fragmented adoption of standards, and limited cross-domain comparability. While today UX and usability evaluation can benefit from continuous technological advances, the field lacks standardized and replicable assessment protocols. Future research should prioritize stronger integration of standards, multimodal evaluation approaches, and longitudinal study designs. Full article

Water supplies contaminated by heavy metals pose a serious threat to human health, especially in areas without access to centralized testing facilities. While copper is a necessary heavy metal in trace levels, high concentrations can have detrimental effects on health, such as oxidative stress, cognitive impairment, and liver damage. Due to their expense, complexity, and reliance on laboratories, conventional detection techniques are accurate but unsuitable for real-time, dispersed deployment. Machine learning offers a potent solution to these constraints by facilitating the automatic, precise, and quick interpretation of complicated sensor data. It makes it possible to make decisions in real time without requiring a large laboratory infrastructure. In this work, a dual-mode optical sensor was developed using the colorimetry and fluorometry images of carbon dots embedded in hydrogels with the Cu²⁺ concentration of 0, 20, 50, 100, 200, and 500 μM. Data augmentation was used to expand the RGB picture dataset for each modality, and these data were interpolated to provide responses at 1 µM intervals (0–500 µM). We trained a comprehensive set of supervised machine learning models, including Logistic Regression, Support Vector Machines, Random Forest, and XGBoost, to categorize water samples into five risk-informed quality levels. The system achieved classification accuracies exceeding 96%. Furthermore, we built a simple user interface to make the system practically deployable in mobile phone. Together, these results demonstrate a scalable, interpretable, cost-effective, and quick solution for real-time water quality monitoring in resource-constrained environments. Since the proposed method focuses on classifying concentration ranges rather than precise quantification, a formal limit of detection (LOD) was not calculated; instead, the lowest concentration in the experimental dataset serves as the minimum detectable level. Full article

Short linear motifs (SLiMs), such as PDZ-binding motifs (PDZbms), are compact interaction modules that mediate transient, specific protein–protein interactions. While PDZbms are well characterized in viral pathogenesis, subverting host protein functions, their role in bacterial systems requires further study. Mycobacterium tuberculosis (Mtb) is an intracellular pathogen that mainly infects macrophages. The type VII secretion system (T7SS) of Mtb secretes a subset of effector proteins (Esx) involved in virulence. By using molecular docking and support vector machine-based prediction, we analyzed PDZbm occurrence in T7SS Esx effector proteins and their ability to bind human PDZ domain-containing proteins. We identified PDZbms in most of the Esx proteins studied, with EsxA and EsxG showing the best PDZ-dependent interaction with syntenin-1, a host scaffold protein involved in vesicular trafficking and immune signaling. Additional Esx proteins were predicted to engage other host PDZ proteins. Proteome-wide analysis of Mtb H37Rv revealed that 23.1% of expressed proteins with ≥50 amino acids contained a C-terminal PDZbm. Gene Ontology and Reactome pathway enrichment revealed their involvement in processes related to bacterial and bacterial–host interactions, including redox balance, immunomodulation, and membrane localization, at various stages of infection. Our results support the existence of a PDZbm-mediated interface between Mtb and the human host, extending the PDZbm mimicry hypothesis beyond viruses to bacterial systems as an immune evasion strategy. This work may open multiple research lines focused on experimental validation and the development of a comparative PDZbm catalogue to uncover conserved virulence mechanisms that may guide the design of host-directed therapeutics. Full article

Eye-tracking is a key enabling technology for smart eyewear, supporting hands-free interaction, accessibility, and context-aware human–machine interfaces under strict constraints on size, power consumption, and computational complexity. While camera-based solutions provide high accuracy, their integration into lightweight and low-power wearable platforms remains challenging. This paper is a feasibility study for the design, simulation, and experimental evaluation of a photosensor oculography (PSOG) eye-tracking system that is fully integrated into an eyewear frame, based on near-infrared (NIR) emitters and photodiodes. The proposed approach combines simulation-driven optimization of the optical constellation, a multi-frequency modulation and demodulation scheme enabling parallel source discrimination and robust ambient-light rejection, and a resource-efficient signal acquisition pipeline suitable for embedded implementation. Eye rotations in azimuth and elevation are inferred from differential reflectance patterns of ocular regions (sclera, iris, and pupil) using lightweight regression techniques, including shallow neural networks and Gaussian process regression, selected to balance estimation accuracy with computational and power constraints. System performance is evaluated using a controllable artificial-eye platform under defined geometric and illumination conditions, enabling repeatable assessment of gaze-estimation accuracy and algorithmic behavior. Sub-degree errors are achieved in this controlled setting, demonstrating the feasibility and potential effectiveness of the proposed architecture. Practical considerations for translation to real-world smart eyewear, including human-subject validation, anatomical variability, calibration strategies, and embedded deployment, are discussed and identified as directions for future work. By detailing the optical design methodology, modulation strategy, and algorithmic trade-offs, this work clarifies the distinct contributions of the proposed PSOG system relative to existing frame-integrated and camera-free eye-tracking approaches, and provides a foundation for further development toward wearable and augmented-reality applications. Full article

The research explored the automation production lines for the bottling of particulate materials in the pharmaceutical industries, covering the integrated processes of loading bottles, filling with particles, sealing, screwing on caps, quality inspection, and storage. The hardware system of the project consists of programmable logic controllers(PLCs), edge servers, motion control equipment, industrial cameras, and mechanical grippers for handling and storage. The aim of this research is to assist the manufacturing industry in transitioning from traditional production models to digital and intelligent production methods. From the perspective of core components, it analyzed and expounded the key technologies for building a digital production line; at the same time, from the perspective of data collection and processing, it clarified the role and advantages of the cloud platform. The product packaging process simulation covers loading bottles, filling with particle materials, sealing, screwing on caps, quality inspection, and storage. The production line issues production instructions and scheduling plans through the human-machine interaction interface and the cloud platform. Full article

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km², accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula. Full article