Search Results (545)

CuZn39Pb3 leaded brass is one of the most widely used alloys in machining, with a 100% machinability index. However, there has been a lack of research on the effects of coldworking on surface integrity (SI) and operating behaviour of CuZn39Pb3 components. This study addresses this knowledge gap by examining the effects of three optimised combined processes on surface roughness, a key SI characteristic. Specifically, samples were subjected to a turning process followed by diamond burnishing (DB); this combined process was performed under three conditions: conventional flood lubrication (F), dry (D), and dry and cool-assisted (D+C) conditions. Cool-assisted conditions were achieved using a special device with a cold air nozzle operating on the vortex tube principle. The D and D+C conditions represent environmentally sustainable alternatives because they eliminate the use of cutting fluids, thereby reducing their adverse effects on both the environment and human health. The resulting surfaces obtained after each of the three optimised combined processes (F, D, and D+C) exhibited mirror-like finishes with minimum average roughness Ra values of 0.054, 0.079, and 0.082 μm, respectively. In addition, the F- and D+C-processes resulted in surface profiles with negative skewness and kurtosis values greater than three. Since roughness shape parameters are known to influence the operating behaviour of machined components, these processes are suitable for improving wear resistance in boundary lubrication regimes. Full article

Pneumatic Artificial Muscles (PAMs) are soft actuators that mimic the contractile behavior of biological muscles through fluid-driven deformation. Originating from McKibben’s 1950s braided design, PAMs have evolved into a diverse class of actuators, offering high power-to-weight ratios, compliance, and safe human interaction, with applications spanning rehabilitation, assistive robotics, aerospace, and adaptive structures. This review surveys recent developments in actuation mechanisms and applications of PAMs. Traditional designs, including braided, pleated, netted, and embedded types, remain widely used but face challenges such as hysteresis, limited contraction, and nonlinear control. To address these limitations, researchers have introduced non-traditional mechanisms such as vacuum-powered, inverse, foldable, origami-based, reconfigurable, and hybrid PAMs. These innovations improve the contraction range, efficiency, control precision, and integration into compact or untethered systems. This review also highlights applications beyond conventional biomechanics and automation, including embodied computation, deployable aerospace systems, and adaptive architecture. Collectively, these advances demonstrate PAMs’ expanding role as versatile soft actuators. Ongoing research is expected to refine material durability, control strategies, and multifunctionality, enabling the next generation of wearable devices, soft robots, and energy-efficient adaptive systems. Full article

Wearable robotic devices for rehabilitation and assistive applications face a critical challenge: discomfort induced by prolonged pressure at the human–robot interface. Conventional attachment systems with static straps or rigid cuffs frequently exceed pain tolerance thresholds, limiting clinical acceptance and patient adherence. This study presents a novel dynamic pressure modulation system using thermally activated Twisted and Coiled Artificial Muscles (TCAMs). The system integrates a lightweight lattice structure (0.1 kg) with biocompatible silicone coating incorporating two TCAMs fabricated from silver-coated nylon 6,6 fibers (Shieldex 235/36 × 4 HCB). Electrothermal activation via 2 A constant current induces axial contraction, dynamically regulating circumferential pressure from 0.05 kgf/cm² to 0.50 kgf/cm² within physiological comfort ranges. Experimental validation on a wrist-worn prototype demonstrates precise pressure control, rapid response (5–10 s), and thermal safety through 8 mm Ecoflex insulation. The system enables on-demand interface stiffening during robotic actuation and controlled pressure release during rest periods, significantly enhancing comfort and device tolerability. This approach represents a promising solution for clinically viable wearable robotic devices supporting upper limb rehabilitation and activities of daily living. Full article

Over-the-counter medication electronic health records (OTC-EHRs) play a significant role in users’ self-medication practices. In this study, we consider the potential advantages of wearable smart devices in health management, along with the information processing capabilities of artificial intelligence (AI), and we propose a conceptual design for an AI-assisted wearable OTC-EHR system. Our objective was to systematically explore the relationship between eHealth literacy, users’ attitudes, and willingness to use the proposed system, as well as to discuss AI interventions. Internet users from China participated in an online survey examining eHealth literacy, subjective attitudes, and motivation to use this conceptual design. Descriptive statistical, correlation, difference, and regression analyses were conducted on 372 valid responses to test the research hypotheses. The results showed that the wearable-device-based OTC-EHR system with AI assistance was accepted by most responders and positively associated with eHealth literacy, which was, in turn, associated with decision-making preferences. This study suggests that AI may be perceived as an auxiliary tool for medication-related decision-making and is associated with the degree of eHealth literacy. Individuals with higher eHealth literacy are more likely to make autonomous decisions, whereas those with lower literacy will potentially rely more on AI support and professional guidance. Full article

Exoskeletons offer promising solutions for enhancing human mobility; however, personalizing assistance parameters to optimize physiological outcomes remains challenging. Human-in-the-loop (HIL) optimization has emerged as an effective strategy for tailoring device control, often using electromyography (EMG) as a real-time proxy for metabolic cost. This study simulates HIL optimization using surrogate models built from the average root mean square of the muscles’ activations (EMG-RMS) derived from treadmill walking trials with a robotic waist tether. Nine surrogate models were evaluated for prediction accuracy, including gradient boosting (GB), random forest, support vector regression, and Gaussian process variants. Seven global optimization algorithms were compared based on convergence time, EMG-RMS at optimum, and efficiency metrics. GB achieved the highest predictive accuracy (1.57% RAEP). Among optimizers, the gravitational search algorithm (GSA) produced the lowest EMG-RMS value (0.17 normalized units) and the fastest convergence (0.32 s), while particle swarm optimization (PSO) achieved 0.36 EMG-RMS in 1.61 s. These findings demonstrate the value of EMG-based simulation frameworks in guiding algorithm selection for HIL optimization, ultimately reducing the experimental burden in developing personalized exoskeleton assistance strategies. Full article

Biomimetic olfactory and taste biosensors replicate human sensory functions by coupling selective biological recognition elements (such as receptors, binding proteins, or synthetic mimics) with highly sensitive transducers (including electrochemical, transistor, optical, and mechanical types). This review summarizes recent progress in olfactory and taste biosensors focusing on three key areas: (i) materials and device design, (ii) artificial intelligence (AI) and data fusion for real-time decision-making, and (iii) pathways for practical application, including hybrid platforms, Internet of Things (IoT) connectivity, and regulatory considerations. We provide a comparative analysis of smell and taste sensing methods, emphasizing cases where integrating both modalities enhances sensitivity, selectivity, detection limits, and reliability in complex environments like food, environmental monitoring, healthcare, and security. Ongoing challenges are addressed with emerging solutions such as antifouling/self-healing interfaces, modular cartridges, machine learning (ML)-assisted calibration, and manufacturing-friendly approaches using scalable microfabrication and sustainable materials. The review concludes with a practical roadmap advocating for the joint development of receptors, materials, and algorithms; establishment of open standards for long-term stability; implementation of explainable/edge AI with privacy-focused analytics; and proactive collaboration with regulatory bodies. Collectively, these strategies aim to advance biomimetic smell and taste biosensors from experimental prototypes to dependable, commercially viable tools for continuous chemical sensing in real-world applications. Full article

Brain–Computer Interface (BCI) technology facilitates direct communication between the human brain and external devices by interpreting brain wave patterns associated with specific motor imagery tasks, which are derived from EEG signals. Although BCIs allow applications such as robotic arm control and smart assistive environments, they face major challenges, mainly due to the large variation in EEG characteristics between and within individuals. This variability is caused by low signal-to-noise ratio (SNR) due to both physiological and non-physiological artifacts, which severely affect the detection rate (IDR) in BCIs. Advanced multi-stage signal processing pipelines, including efficient filtering and decomposition techniques, have been developed to address these problems. Additionally, numerous feature engineering techniques have been developed to identify highly discriminative features, mainly to enhance IDRs in BCIs. In this review, several pre-processing techniques, including feature extraction algorithms, are critically evaluated using deep learning techniques. The review comparatively discusses methods such as wavelet-based thresholding and independent component analysis (ICA), including empirical mode decomposition (EMD) and its more sophisticated variants, such as Self-Adaptive Multivariate EMD (SA-MEMD) and Ensemble EMD (EEMD). These methods are examined based on machine learning models using SVM, LDA, and deep learning techniques such as CNNs and PCNNs, highlighting key limitations and findings, including different performance metrics. The paper concludes by outlining future directions. Full article

Understanding and accurately estimating cognitive workload is crucial for the development of adaptive, user-centered interactive systems across a variety of domains including augmented reality, automotive driving assistance, and intelligent tutoring systems. Cognitive workload assessment enables dynamic system adaptation to improve user experience and safety. In this work, we introduce a novel framework that leverages smooth pursuit eye movements as a non-invasive and temporally precise indicator of mental effort. A key innovation of our approach is the development of trajectory-independent algorithms that address a significant limitation of existing methods, which generally rely on a predefined or known stimulus trajectory. Our framework leverages two solutions to provide accurate cognitive load estimation, without requiring knowledge of the exact target path, based on Kalman filter and B-spline heuristic classifiers. This enables the application of our methods in more naturalistic and unconstrained environments where stimulus trajectories may be unknown. We evaluated these algorithms against classical supervised machine learning models on a publicly available benchmark dataset featuring diverse pursuit trajectories and varying cognitive workload conditions. The results demonstrate competitive performance along with robustness across different task complexities and trajectory types. Moreover, our framework supports real-time inference, making it viable for continuous cognitive workload monitoring. To further enhance deployment feasibility, we propose a federated learning architecture, allowing privacy-preserving adaptation of models across heterogeneous devices without the need to share raw gaze data. This scalable approach mitigates privacy concerns and facilitates collaborative model improvement in distributed real-world scenarios. Experimental findings confirm that metrics derived from smooth pursuit eye movements reliably reflect fluctuations in cognitive states induced by working memory load tasks, substantiating their use for real-time, continuous workload estimation. By integrating trajectory independence, robust classification techniques, and federated privacy-aware learning, our work advances the state of the art in adaptive human–computer interaction. This framework offers a scientifically grounded, privacy-conscious, and practically deployable solution for cognitive workload estimation that can be adapted to diverse application contexts. Full article

Population aging and rising rehabilitation demands highlight the need for advanced assistive devices to improve mobility in individuals with motor impairments. Existing back-support lifting nursing beds often lack adequate human–machine adaptability, safety, and emotional consideration. This study presents a human-centered, data-driven optimization pipeline that integrates behavior–emotion dual recognition, simulation verification, and parameter optimization with user demand mining, biomechanical simulation, and sustainable practices. The design utilizes GreenAI, focusing on low-power algorithms and eco-friendly materials, ensuring energy-efficient AI models and reducing the environmental footprint. A dual-channel data fusion method was developed, combining movement parameters from sit-to-lie transitions with emotional needs extracted from e-commerce reviews using the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) models. The fuzzy Kano model prioritized design objectives, identifying multi-position adjustment, joint protection, armrest optimization, and interaction comfort as key targets. An AnyBody-based human–device model quantified muscle (erector spinae, rectus abdominis, trapezius) and hip joint loads during posture changes. Simulations verified the design’s ability to improve load distribution, reduce joint stress, and enhance comfort. The optimized nursing bed demonstrated improved adaptability across user profiles while maintaining functional reliability. This framework offers a scalable paradigm for intelligent rehabilitation equipment design, with potential extension toward AI-driven adaptive control and clinical validation. This sustainable methodology ensures that the device not only meets rehabilitation goals but also contributes to a more environmentally responsible healthcare solution, aligning with global sustainability efforts. Full article

Lower limb exoskeletons significantly enhance human functionality. However, simultaneously improving the load capacity of these devices while reducing metabolic costs presents a major challenge in the industry. This paper presents a lower limb exoskeleton that integrates both rigid and flexible structures to facilitate active assistance and passive load transfer at the hip joint. The load transfer experiments were conducted with weights of 10 kg and 15 kg. During static standing, the load transfer rates were recorded at 90.48% and 69.70%, respectively. In dynamic walking, these rates decreased to 62.07% and 43.69%. Furthermore, in metabolic experiments involving a load of 15 kg, metabolic costs in the exoskeleton assistance modes OFF (Assist OFF) and exoskeleton assistance ON (Assist ON) were reduced by 8.3% and 21.61%, respectively, compared to the exoskeleton-free condition (NE). Furthermore, the Assist ON mode further decreased metabolic costs by 13.22% compared to the Assist OFF mode. These findings demonstrate that the rigid-soft coupled lower limb exoskeleton exhibits exceptional load transfer capabilities and effective assistance, highlighting its potential to enhance human performance in weight-bearing activities. Full article

In this review we offer a guide to organ-on-chip (OoC) technologies, covering the full experimental pipeline, from organoid derivation and culture, through microfluidic device fabrication and design strategies, to perfusion systems and data acquisition with AI-assisted analysis. At each stage, we highlight both the advantages and limitations, providing a balanced perspective that aids experimental planning and decision-making. By integrating insights from stem cell biology, bioengineering, and computational analytics, this review presents a compilation of the state of the art of OoC research. It emphasizes practical considerations for experimental design, reproducibility, and functional readouts while also exploring applications in human and veterinary medicine. Furthermore, key technical challenges, standardization issues, and regulatory considerations are discussed, offering readers a clear roadmap for advancing both foundational studies and translational applications of OoC systems. Full article

With the global ageing population and the increasing prevalence of mobility impairments, the demand for effective and comfortable rehabilitation and assistive solutions has grown rapidly. Soft exoskeletons have emerged as a key direction in the development of wearable rehabilitation devices. This review examines how these systems are designed and controlled, as well as how they differ from the rigid exoskeletons that preceded them. Made from flexible fabrics and lightweight components, soft exoskeletons use pneumatic or cable mechanisms to support movement while keeping close contact with the body. Their compliant structure helps to reduce joint stress and makes them more comfortable for long periods of use. The discussion in this paper covers recent work on lower-limb designs, focusing on actuation, power transmission, and human–robot coordination. It also considers the main technical barriers that remain, such as power supply limits, the wear and fatigue of soft materials, and the challenge of achieving accurate tracking performance, low latency, and resilience to external disturbances. Studies reviewed here show that these systems help users regain functionality and improve rehabilitation, while also easing caregivers’ workload. The paper ends by outlining several priorities for future development: lighter mechanical layouts, better energy systems, and adaptive control methods that make soft exoskeletons more practical for everyday use as well as clinical therapy. Full article

This paper presents the design and performance of Table ASSIST-EW, a portable adaptable user-friendly cable-driven device that can be used on a table to support elbow and wrist exercises. This device is intended for older adults who experience arm weakness due to aging. Table ASSIST-EW has been developed based on results from testing and practical insights from biomechanics and robotics to address challenges in human–robot interaction that limit the use of assistive technologies. Table ASSIST-EW is designed to assist natural arm movements during motion exercise and rehabilitation, making the motion assistance easy and easily engaged for users. The design process is explained starting from identifying user needs up to the creation of a prototype. A key feature of Table ASSIST-EW is its cable-driven actuation system. The design is inspired by a previous device, L-CADEL, which went through several design revisions. The lessons learned from L-CADEL’s development and test experiences suggested design solutions for Table ASSIST-EW’s structure, function, and use. This paper discusses the background, design requirements, system development, and performance evaluation. The results show that the Table ASSIST-EW device meets important goals in usability and functionality, making it a promising solution for robotic rehabilitation and motion exercise for the elderly. Full article

Hand Gesture Recognition (HGR) is a vital technology that enables intuitive human–computer interaction in various domains, including augmented reality, smart environments, and assistive systems. Achieving both high accuracy and real-time performance remains challenging due to the complexity of hand dynamics, individual morphological variations, and computational limitations. This paper presents a lightweight and efficient skeleton-based HGR framework that addresses these challenges through an optimized multi-stream Convolutional Neural Network (CNN) architecture and a trainable ensemble tuner. Dynamic 3D gestures are transformed into structured, noise-minimized 2D spatiotemporal representations via enhanced data-level fusion, supporting robust classification across diverse spatial perspectives. The ensemble tuner strengthens semantic relationships between streams and improves recognition accuracy. Unlike existing solutions that rely on high-end hardware, the proposed framework achieves real-time inference on consumer-grade devices without compromising accuracy. Experimental validation across five benchmark datasets (SHREC2017, DHG1428, FPHA, LMDHG, and CNR) confirms consistent or superior performance with reduced computational overhead. Additional validation on the SBU Kinect Interaction Dataset highlights generalization potential for broader Human Action Recognition (HAR) tasks. This advancement bridges the gap between efficiency and accuracy, supporting scalable deployment in AR/VR, mobile computing, interactive gaming, and resource-constrained environments. Full article

This study presents the development of a prototype anthropomorphic soft robotic gripper intended for applications in rehabilitation and assistive robotics, where safe and adaptive interaction with humans is required. The device consists of three elastomeric fingers, fabricated in TPU via FFF 3D printing and actuated through pneumatic soft actuators that ensure compliant contact with both biological tissue and rigid objects. A custom 3D-printed pneumatic rotary actuator enables finger reconfiguration, thereby extending the range of grasping modalities. The actuation system comprises six 2/2 solenoid valves controlled by an Arduino Uno and integrated into a dedicated pneumatic circuit. Experimental characterization demonstrated a peak grasping force exceeding 17 N on rigid targets, while functional tests in table-picking scenarios confirmed adaptability to objects of varying shapes and sizes. Owing to its anthropomorphic configuration, mechanical compliance, and ease of fabrication and control, the proposed gripper represents a versatile solution for rehabilitation-oriented devices as well as assistive robotic end-effectors in pick-and-place tasks. Full article