Search Results (795)

Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton© and validated using synchronous EEG–EMG acquisition via the LiveAmp platform©, a commercially available platform that was not developed specifically for this study. A hybrid CNN–LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user’s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque–angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device’s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain–machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human–robot applications. Full article

Flexible capacitive pressure sensors have garnered significant attention in wearable electronics and robotic tactile sensing due to their high flexibility and simple structure. However, non-uniform distribution of conductive fillers in composite dielectric layers often compromises dielectric stability and sensing performance. In this work, a Cu/PDMS composite dielectric layer was fabricated using ultrasonic-assisted homogenization to enhance Cu particle dispersion and suppress sedimentation. A theoretical model and finite element simulations were employed to investigate the effects of particle distribution on permittivity, capacitance, electric field, and current density. The results indicate that uniform Cu dispersion improves dielectric stability and mitigates local electric-field concentration. Compared with conventionally prepared sensors, the ultrasonically treated sensor demonstrated higher sensitivity, enhanced dielectric stability, and a broader working range. Specifically, the sensor achieved a sensitivity of 0.157 kPa⁻¹ within 0–1 kPa and maintained stable performance over 1000 loading cycles. These findings confirm that ultrasonic-assisted homogenization is an effective approach for improving the dielectric and sensing performance of flexible capacitive pressure sensors. Full article

Resolving the kinematic redundancy of 7-DoF humanoid arms to generate natural, human-like motions remains a fundamental challenge in biomimetic robotics. This paper presents a hybrid inverse kinematics (IK) framework that learns a pose-dependent redundancy parameter and integrates it into a differential IK solver. Specifically, we employ the stereographic Shoulder–Elbow–Wrist (SEW) angle as a well-conditioned geometric parameterization. This formulation transforms the algorithmic singularity into a unidirectional half-line, which can be oriented outside the typical reachable workspace. To specify the optimal configuration within the self-motion manifold, a motion dataset was collected by teleoperating a humanoid arm via an anthropomorphic wearable exoskeleton. This approach translates operator-specific postural preferences into the robot’s joint space. A lightweight neural network was then trained to learn the mapping from end-effector poses to these operator-specific SEW angles. By incorporating the predicted SEW angle as a dynamic secondary objective in the null space of the primary tracking task, the proposed framework enables natural redundancy resolution while preserving end-effector tracking accuracy. Both simulations and real-robot experiments were conducted to validate the approach. Results show that, compared to the average performance of static fixed-parameter strategies, the proposed method improves the Joint Configuration Quality Index (CQI) by 22.5% and reduces energy costs by 11.3%. Moreover, the sub-millisecond inference latency (0.44 ms) facilitates seamless integration into real-time control pipelines. Full article

Multimodal sensor fusion is increasingly used to improve observability for fault diagnosis and prognostics, enabling Remaining Useful Life estimation in complex mechatronic and robotic systems. Yet, real-world deployments remain vulnerable to sensor faults and data integrity issues—including bias and drift, miscalibration, dropouts, saturation, cross-talk, time desynchronization, and domain shift—which can propagate through fusion pipelines and lead to optimistic validation and poor generalization. These challenges are particularly consequential in safety- and health-adjacent applications such as collaborative robots, wearable/rehabilitation devices, and human-centric mechatronic systems where decisions based on faulty sensing may affect both reliability and user safety. This review synthesizes the state of the art on (i) sensor fault taxonomies and fault models relevant to multimodal fusion, (ii) fault-aware fusion strategies spanning data-, feature-, and decision-level integration, and (iii) how sensor faults and uncertainty impact diagnosis and remaining-life estimators. We will conduct a systematic scoping review of peer-reviewed literature, extracting sensor modalities, fault characterization or injection protocols, fusion architectures, validation settings (simulation, hardware-in-the-loop, bench, and in-field/on-body studies), and reporting completeness. Beyond summarizing methods, we provide practical reporting standards for sensor-fusion-based diagnosis and prognostics, including a minimum disclosure set covering synchronization, fault ground truth, missingness handling, leakage controls, uncertainty calibration, and task-relevant metrics. Reusable checklists and evidence tables are included to support more comparable, reproducible, and deployment-ready research. Full article

Robotic assistive devices, such as exoskeletons, are increasingly employed in walking rehabilitation. Therefore, the measurement of both movement kinematics and cognitive workload is important to understand this human–robot interaction in real-world contexts. To address this need this study presents the validation of a framework integrating inertial motion capture (Xsens) and eye-tracking sensor (Pupil Neon) within a Mixed Reality (Meta Quest 3) architecture. We developed an overground dual-task paradigm in which holographic numbers appear in the user’s peripheral vision. This setup actively stimulates visuospatial attention while quantifying kinematic and cognitive output. To validate the framework, the protocol has been tested on 30 healthy subjects across repeated exoskeleton training sessions. Statistical analyses revealed that the Coefficient of Multiple Correlation (CMC) and Spectral Arc Length (SPARC), calculated on the shank angular velocity, together with the Step Length Variability, exhibited significant time effects (p < 0.01), mapping the transition toward automated gait. Concurrently, pupillometric data demonstrated a measurable reduction in neurocognitive demand; specifically, the Task-Evoked Pupillary Response (TEPR) decreased significantly across progressive training sessions (p < 0.05). With this work, we validated a measurement protocol that aims to provide a novel methodology for objectively evaluating motor and cognitive adaptation in wearable assistive devices. Full article

Artificial muscles are an emerging class of actuators designed to mimic the compliant, efficient, and versatile behavior of biological muscles for fields including the following: soft robotics, prosthetics, wearable enhancements, haptic interfaces, and biomedical devices. These systems encompass various actuation mechanisms, including pneumatic, hydraulic, thermal, ionic, electrochemical, and electrostatic. Each with distinct tradeoffs in voltage, strain, output force, bandwidth, efficiency, and manufacturability. Among them, electrostatic actuators have attracted increased attention due to their fast response times, high energy densities, strong compatibility with soft materials, and scalability from microscale devices to large-area and stacked actuators. However, challenges such as dielectric breakdown, material fatigue, and fabrication complexity continue to limit widespread deployment. This review presents a structured classification of various artificial muscle technologies and an in-depth examination of electrostatic actuators including dielectric elastomers, electrostrictive and ferroelectric polymers, liquid crystal elastomers, electrostatic film motors, stacked architectures, and microscale/milliscale devices. In this review the operating principles, materials, architectures, performance characteristics, and failure modes of electrostatic actuators will be discussed. Additionally, a comparison will highlight tradeoffs across actuator families based on metrics such as voltage, force, strain, bandwidth, and manufacturability. Lastly, we outline future research directions in materials, physics-informed modeling, system integration, and scalable fabrication necessary to advance electrostatic artificial muscles toward practical, real-world deployment. Full article

Background/Objectives: Robot-assisted surgery (RAS) has grown rapidly over the past few decades. To determine the effect of high stress levels on the performance of RAS, monitoring some parameters of surgeons is critical. This can be aided by the development of Artificial Intelligence (AI), which has exponentially grown in recent years. This study aims to predict the surgeon’s stress level based on ergonomic, kinematic and physiological parameters of the surgeon obtained in the immediately previous situation during RAS activities. Methods: Physiological data were recorded from surgeons during twenty-six surgical sessions involving twelve participants with different levels of experience and surgical specialties. After dataset generation, two preprocessing procedures (scaling and normalization) were applied to the recorded signals. The processed data were then partitioned into two subsets: 80% of the samples were used for model training and cross-validation, while the remaining 20% were reserved for testing. Six AI approaches were evaluated to build predictive models: multiple linear regression (MLR), a support vector machine (SVM), a multilayer perceptron (MLP), a convolutional neural network (CNN), random forest (RF), and a U-Net algorithm (UNET). These algorithms were trained using the training dataset and subsequently assessed on the independent test set. In addition, after each surgical session, surgeons completed a questionnaire reporting their perceived stress level, which was later compared with the stress estimates generated by the predictive models. Results: The results obtained showed that MLR and scaling pre-processing reached the highest R² coefficients and the lowest error for each studied parameter. The results of the surgeons’ surveys were highly correlated for microsurgery activities (R² = 0.7989) and for laparoscopy RAS (R² = 0.8381). Conclusions: The linear models proposed were correctly validated on cross-validation and the test dataset. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon’s health during RAS. Full article

Wearable pressure-sensing orthoses are increasingly used in neurorehabilitation to support gait recovery, monitor plantar pressure distribution, and improve patient mobility during repetitive therapy sessions. The performance of these devices depends strongly on the materials used in the skin-contact layer, since material properties influence comfort, flexibility, durability, and force transmission during daily use. This study proposes a hybrid multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP) and the VIKOR method for material selection in sensor-integrated plantar orthoses. Five candidate materials, ethylene vinyl acetate (EVA), polyethylene (PE), polyurethane (PU), cobalt–chromium–molybdenum alloy (CoCrMo), and polypropylene (PP), were evaluated using five criteria: comfort and skin compatibility, elasticity, fatigue resistance, density, and energy dissipation. AHP was applied to determine the relative importance of the evaluation criteria using expert judgment, while VIKOR was used to rank the material alternatives and identify the compromise solution. The results showed that polyurethane achieved the best overall performance due to its balanced behavior in comfort, elasticity, and fatigue resistance, which are essential properties for long-term wearable neurorehabilitation devices. A sensitivity analysis confirmed that moderate variations in expert weighting did not modify the final ranking. Compared with conventional selection approaches based mainly on isolated material properties, the proposed framework offers a clear and reproducible method for integrating mechanical and user-related requirements into the material selection process for wearable orthoses. Full article

Soft robotics is a rapidly evolving field that has attracted significant attention within the scientific community. This review analyzes the main advantages of pneumatic technology in service robots across the different application domains defined by the International Federation of Robotics (IFR). By organizing the literature according to application domains, this work aims to clarify the specific benefits of pneumatic and soft pneumatic solutions in each context. The proposed approach distinguishes between traditional pneumatic solutions and the subsequent emergence of soft robotics, in order to highlight how and to what extent soft technologies have reshaped the design and application scenarios. Particular attention is devoted to the role of materials and recent manufacturing techniques used by researchers to fabricate soft pneumatic robots. Based on 163 selected papers, the analysis reveals that medical and agricultural applications dominate soft pneumatic research, accounting for 41% and 27% of the soft sample, respectively. Compared to traditional pneumatics, the medical sector has expanded into cardiac assistive devices, wearable monitoring sensors, and minimally invasive surgery; agriculture has grown from 17% to 27% of the soft literature due to precision harvesting grippers. Soft inspection robots have increased thanks to continuum manipulators and bio-inspired locomotion, while search and rescue remains a niche (9%) but promising sector. Unlike previous reviews that focus on single domains or technologies, this work quantifies the uneven transition from rigid to soft pneumatics across IFR sectors and highlights emerging application-specific design paradigms that were not feasible with traditional systems. Full article

Composite materials, particularly polymers reinforced with carbon, glass, and aramid fibers, enable the development of lightweight yet mechanically robust structures that enhance user comfort and functional performance. Their high strength-to-weight ratio and fatigue resistance make them ideal for applications requiring repetitive movements in rehabilitation and assistive robotics. However, challenges remain related to cost-effective production, durability under complex loading conditions, and ergonomic fit to human anatomy. Recent advances in materials science and smart materials are expanding the possibilities of multifunctional composites with embedded sensors. Furthermore, machine learning methods are increasingly being used to optimize material selection and structural design. Future advances are expected to improve scalability, personalization, and system integration, positioning composite fibers as a key assistive technology in next-generation robotic systems. Full article

The development of robotic systems that can operate safely and adaptively alongside humans requires actuators that combine compliance with reliable performance. Soft pneumatic rotary actuators (SPRAs) have emerged as promising candidates due to their inherent compliance, lightweight design, and capability to generate smooth rotational motion through elastic deformation. However, the diverse designs and performance characteristics of SPRAs make it challenging to identify optimal configurations for specific applications. This review comprehensively surveys current SPRAs, focusing on structural designs, materials, and fabrication methods. While SPRAs offer advantages such as reduced risk of injury and enhanced adaptability, significant challenges remain in optimizing torque output, rotational range, and durability. By comparing existing designs and highlighting open research challenges, this paper aims to guide the advancement of SPRAs, facilitating their integration into safe, effective robotic systems for industrial, medical, and wearable applications. Full article

Textile-based flexible pressure sensors have attracted considerable attention in wearable sensing applications due to their good comfort and mechanical compatibility. However, their sensitivity usually exhibits a nonlinear dependence on pressure, while a compact analytical framework with interpretable physical parameters is still lacking. In this work, a simplified physical model based on lumped effective parameters was established based on the evolution of fiber–conductive particle contacts, and an expression describing the sensitivity–pressure relationship was derived. The model indicates that the sensitivity is mainly governed by an electrical parameter α and a mechanical parameter ratio $E_b/E_x$, and captures the dominant nonlinear decrease in sensitivity with increasing pressure. To verify the applicability of the model, the effects of conductive particle loading, filler type, surface treatment, sensing-layer area, weave structure, and layer number on the sensor response were systematically investigated. In addition, comparison between model-based calculation and experiment in the low- and medium-pressure range gave RMSE values of 0.0040 and 0.0056, and MRE values of 27.6% and 13.4% for the single-layer and four-layer structures, respectively. These results show that the proposed framework captures the main trends of the sensitivity–pressure behavior and provides a physically interpretable basis for discussing how structural and material factors regulate sensor response. This work offers a useful framework for understanding the structure–property relationship of textile-based piezoresistive pressure sensors and may provide preliminary guidance for the design of customized sensors in wearable healthcare and soft robotics applications. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

In recent years, hand exoskeleton robots have attracted extensive attention from researchers and practitioners due to their potential to rehabilitate, assist, and enhance hand movements, particularly for stroke patients. With an ageing population increasingly affected by strokes, there is a growing demand for patient-centred interventions which place less demand on clinicians, especially wearable devices that can enhance hand function. Advances in artificial intelligence have opened new avenues for developing more reliable and adaptive assistive systems. This study presents a systematic literature review, following the PRISMA protocol on the design elements of hand exoskeleton robots, acknowledging the emerging perspectives on AI integration and ethical considerations. The study provides a comprehensive foundation for future research and development in rehabilitation technologies by systematically synthesising the current mechanical architecture, actuation, sensors, material, weight, and cost aspects of soft hand exoskeleton robots for rehabilitation. The results show important patterns and trade-offs in various design dimensions, providing useful information to direct the development of more accessible and efficient rehabilitation solutions in the future. Full article

Printable piezoelectric materials are emerging as a cornerstone of next-generation sensing, actuation, and energy harvesting technologies, driven by the need for lightweight, flexible, and digitally manufactured transducers. Conventional ceramic piezoelectrics offer exceptional electromechanical performance but require high-temperature sintering and exhibit intrinsic brittleness, limiting their integration with soft or unconventional substrates. Polymeric piezoelectrics, in contrast, provide mechanical compliance and low-temperature processability yet suffer from lower crystallinity, reduced piezoelectric coefficients, and limited thermal stability. These contrasting characteristics have catalyzed the development of functional piezoelectric inks—ceramic, polymeric, and hybrid formulations engineered for additive manufacturing techniques such as direct ink writing, stereolithography, screen printing, and inkjet printing. This review systematically examines the material compositions, dispersion chemistries, printing requirements, thermal treatment pathways, and poling strategies that govern the performance of printed piezoelectric transducers. By comparing ceramic-based, polymer-based, and hybrid systems, we reveal the fundamental trade-offs between printability, crystallinity, mechanical compliance, and electromechanical response, and map how these trade-offs shape device design across wearable electronics, soft robotics, and structural health monitoring. Finally, we highlight emerging approaches—including surface functionalization, low-temperature crystallization, liquid-phase sintering, and engineered ceramic–polymer interfaces—that offer promising routes to bridge the gap between printability and high piezoelectric performance. Full article