Search Results (1,301)

Due to the large emissions of greenhouse gases from the burning of fossil fuels and people’s demand for green materials and energy, the development of environmentally friendly triboelectric nanogenerators (TENGs) is becoming increasingly significant. Silk fibroin (SF) is considered an ideal biopolymer candidate for fabricating green TENGs due to its biodegradability and renewability. However, its intrinsic brittleness and relatively weak triboelectric performance severely limit its practical applications. In this study, SF was physically blended with poly(ethylenimine) (PEI), a polymer rich in amino groups, to fabricate SF/PEI composite films. The resulting films were employed as tribopositive layers and paired with a poly(tetrafluoroethylene) (PTFE) tribonegative layer to assemble high-performance TENGs. Experimental results revealed that the incorporation of PEI markedly enhanced the flexibility and electron-donating capability of composite films. By optimizing the material composition, the SF/PEI-based TENG achieved an open-circuit voltage as high as 275 V and a short-circuit current of 850 nA, with a maximum output power density of 13.68 μW/cm². Application tests demonstrated that the device could serve as an efficient self-powered energy source, capable of lighting up 66 LEDs effortlessly through simple hand tapping and driving small electronic components such as timers. In addition, the device can function as a highly sensitive self-powered sensor, capable of generating rapid and distinguishable electrical responses to various human motions. This work not only provides an effective strategy to overcome the intrinsic limitations of SF-based materials but also opens up new avenues for the development of high-performance and environmentally friendly technologies for energy harvesting and sensing. Full article

This paper presents the development of a 32-degree-of-freedom (DoF) humanoid robotic system designed for autonomous piano performance. The system integrates a vision-based music sheet reader with a YOLOv8 neural network for real-time detection and classification of musical symbols, achieving a mean average precision (mAP) of 96% at IoU 0.5. A heuristic-based synchronization and motion planning module computes optimal finger trajectories and hand placements, enabling expressive and temporally accurate performances. The robotic hardware comprises two anthropomorphic hands mounted on linear rails, each with independently actuated fingers capable of vertical, horizontal, and rotational movements. Experimental validation demonstrates the system’s ability to execute complex musical passages with precision and synchronization. Limitations related to dynamic expressiveness and symbol generalization are discussed, along with proposed enhancements for future iterations. The results highlight the potential of AI-driven robotic systems in musical applications and contribute to the broader field of intelligent robotic performance. Full article

Background: Children with spastic hand impairments resulting from cerebral palsy or neuromuscular disorders often exhibit a restricted range of motion and diminished functional use. Rehabilitation devices that assist joint mobilization can enhance therapeutic outcomes, yet few solutions target pediatric populations. Methods: This study aimed to design, implement, and preliminarily evaluate a biomechanical device tailored to promote flexo-extension, radial–ulnar deviation, and supination movements in spastic hands of school-aged children. A prototype combining a motor-driven actuation system, adjustable wrist and finger supports, and a MATLAB-based graphical user interface was developed. Two participants (aged 8 and 10) with clinically diagnosed spastic hemiparesis underwent 25-minute sessions over 15 consecutive days. Joint angles were recorded before and after each session using an electro-goniometer. Data normality was assessed via the Shapiro–Wilk test, and pre–post differences were analyzed with the Wilcoxon signed-rank test (α = 0.05). Results: Both participants demonstrated consistent increases in their active range of motion across all measured planes. Median flexo-extension improved by 12.5° (p = 0.001), ulnar–radial deviation by 7.3° (p = 0.002), and supination by 9.1° (p = 0.001). No adverse events occurred, and device tolerance remained high throughout the intervention. Conclusions: The device facilitated statistically significant enhancements in joint mobility in a small pediatric cohort, supporting its feasibility and safety in spastic hand rehabilitation. These preliminary findings warrant larger controlled trials to confirm the device’s efficacy, optimize treatment protocols, and assess its long-term functional benefits. Full article

This study investigates the effectiveness of a virtual reality (VR) simulation for teaching the hand lay-up process in composite manufacturing within mechanical engineering education. A within-subjects experiment involving 17 undergraduate mechanical engineering students compared the VR-based training with conventional physical laboratory instruction. Task performance, cognitive load, and learner perceptions were measured using procedural accuracy scores, completion times, NASA-TLX workload ratings, and post-task interviews. Results indicated that while participants required more time to complete the task in VR, procedural accuracy was comparable between VR and physical labs. VR significantly reduced mental, physical, and effort-related demands but elicited higher frustration levels, primarily due to navigation challenges and motion discomfort. Qualitative feedback showed strong learner preference for VR, citing its hazard-free environment, repeatability, and step-by-step guidance. These findings suggest that VR offers a viable and pedagogically effective alternative or complement to traditional composite-manufacturing training, particularly in contexts where access to physical facilities is limited. Future work should examine long-term skill retention, incorporate haptic feedback for tactile realism, and explore hybrid models combining VR and physical practice to optimise learning outcomes. Full article

Sign language recognition aims to capture and classify hand and arm motion signals to enable intuitive communication for individuals with hearing and speech impairments. This study proposes a real-time Chinese Sign Language (CSL) recognition framework that integrates a dual-stage segmentation strategy with a lightweight three-layer artificial neural network to achieve early gesture prediction before completion of motion sequences. The system was evaluated on a 21-class CSL dataset containing several highly similar gestures and achieved an accuracy of 91.5%, with low average inference latency per cycle. Furthermore, training set truncation experiments demonstrate that using only the first 50% of each gesture instance preserves model accuracy while reducing training time by half, thereby enhancing real-time efficiency and practical deployability for embedded or assistive applications. Full article

Background: Glenohumeral arthrodesis (GHA) and extraplexal tendon transfers (TT) have been described as options for secondary shoulder stabilization and reanimation following adult traumatic brachial plexus injury (BPI) with delayed presentation or failure of primary nerve reinnervation. This study aimed to evaluate the outcomes and complication profiles of these two approaches to shoulder reanimation to better understand the indications, anticipated outcomes, and complication risks of each for traumatic brachial plexus injury. Methods: A systematic search of six databases (PubMed, EMBASE, SCOPUS, CINAHL, SPORTDiscus, Cochrane Library) was conducted in March 2025 following PRISMA guidelines. Studies reporting clinical outcomes in adults undergoing GHA or TT for traumatic BPI were included. Pooled mean range of motion and proportional complication and reoperation rates were calculated using random- and fixed-effects models, as appropriate. Results: A total of 22 studies involving 269 TT procedures and 194 GHA procedures were analyzed. Mean shoulder abduction was 81° (95% CI 54–108°) in the TT group and 51° (95% CI 37–65°) in the GHA group. Mean forward flexion was 88° (95% CI 51–124°) in the TT group and 56° (95% CI 44–68°) in the GHA group. The pooled complication rate was 4.8% (95% CI 2.6–8.6%) after TT and 26.4% (95% CI 18.5–36.1%) after GHA. The pooled reoperation rate was 3.2% (95% CI 1.5–6.6%) after TT and 17% (95% CI 10.8–25.7%) after GHA. Notably, TT cohorts generally had shorter follow-up durations, which may underrepresent late complications or reoperations. Conclusions: TT results in significantly lower complication and reoperation rates and demonstrates similar range-of-motion outcomes compared to GHA, suggesting that TT can be considered a first-line salvage option for motion preservation, while GHA remains an option for persistent instability, pain, or inability to achieve functional positioning of the hand in patients with traumatic BPIs. Additional comparative studies with higher levels of evidence are warranted to validate these findings. Full article

Modern myoelectric prostheses remain difficult to control, particularly during rehabilitation, leading to high abandonment rates in favor of static devices. This highlights the need for advanced controllers that can automate some motions. This study presents an end-to-end framework coupling deep reinforcement learning with augmented reality (AR) for prosthetic actuation. A 14-degree-of-freedom hand was modeled in Blender and deployed in Unity. Two reinforcement learning agents were trained with distinct reward functions for a grasping task: (i) a discrete, Booleann reward with contact penalties and (ii) a continuous distance-based reward between joints and the target object. Each agent trained for 3 × 10⁷ timesteps at 50 Hz. The Booleann reward function performed poorly by entropy and convergence metrics, while the continuous reward function achieved success. The trained agent using the continuous reward was integrated into a dynamic AR scene, where a user controlled the prosthesis via a myoelectric armband while the grasping motion was actuated automatically. This framework demonstrates potential for assisting patients by automating certain movements to reduce initial control difficulty and improve rehabilitation outcomes. Full article

This paper presents research and theoretical development of a mathematical model that, first, allows us to understand how the positional exactitude of the output link of a four-bar mechanism depends on the manufacturing dimensional tolerances. To find this dependence, the total differentials of the kinematic constraint functions that govern the field of positions must be determined for each kinematic cycle of the mechanism under consideration. These total differentials lead to a system of equations whose solution gives the positional errors of the movable output links as a function of the manufacturing dimensional errors and an incidence matrix that varies with each one of the positions of the input element. On the other hand, the theoretical transmission ratio between the output velocities with respect to the input velocity of the articulated kinematic chain is defined, and for determining the total errors in each ratio, the total differential of each one of them is calculated, showing a clear dependence with respect to the positional errors of the output links (previously defined) of the mechanism. The sum of the theoretical transmission ratio and its respective error provides the real transmission ratio. Furthermore, the described methodology allows for determining the sensitivity (influence coefficients) in the transmission ratios due to errors inherent in the link lengths. Finally, the presented analytical approach is numerically implemented through an example of articulated parallelogram design, principally characterizing in graphic form the transmission ratios in their regions of permitted movements and blocking positions, for a specific IT degree of precision of the bilateral dimensional tolerances of their functional geometric parameters, with the objective of analyzing every aspect related to the performance of the mechanisms. This formalism is validated through three particular design cases using a CAD model in a simulation module of kinematic motion analysis; additionally, the evolution of the transmission angle is discussed. The methods and conclusions proposed in this document also leave open the way as future work to study separately the magnitudes and signs of the positional errors and the transmission ratio, or even the influence coefficients themselves, in order to assign the most convenient degree of IT precision for each link in the mechanism with the purpose of reducing errors in the designs and obtain better efficiency in the transmission ratio. Full article

Home-based rehabilitation supports neuromuscular patients while minimising the need for extensive clinical supervision. Due to a growing number of stroke survivors, this approach appears to be more practical for patients across diverse demographics. Although existing hardware-based assistive devices are pretty common, they have limitations in terms of usability, wearability, and safety, as well as other technical constraints such as bulkiness and torque-to-weight ratios. To overcome these issues, soft actuator–based assistance prioritises user safety and ergonomics, as it is more wearable and lightweight, offering overall support while reducing the social stigma associated with disability. Among the existing soft actuation techniques and related materials, shape memory alloys (SMA) present a feasible option, offering current-controlled actuation and compatibility with integration into flexible textiles, thereby enhancing the wearability of the device. This paper presents a compact, SMA-driven assistive device designed to support natural motion, reduce muscle fatigue, and enable daily therapy. Embedded in a glove, the device allows mirrored control, where one hand’s movement assists the other, using flex sensors for feedback. The functionality of the electromyography (EMG) sensor is also used to evaluate the activation of the SMA wire; however, it is not employed for detecting individual finger motions in the assistive hand. Polyurethane foam insulation minimises thermal effects while maintaining lightweight wearability. Experimental results demonstrate a reduction in actuation time at higher voltages and the effective lifting of light to moderate weights. The device shows strong potential for affordable, home-based rehabilitation and everyday assistance. Full article

Eye movement is an important tool used to investigate cognition. It also serves as input in human–computer interfaces for assistive technology. It can be measured with camera-based eye tracking and electro-oculography (EOG). EOG does not rely on eye visibility and can be measured even when the eyes are closed. We investigated the feasibility of detecting the gaze direction using EOG while having the eyes closed. A total of 15 participants performed a proprioceptive calibration task with open and closed eyes, while their eye movement was recorded with a camera-based eye tracker and with EOG. The calibration was guided by the participants’ hand motions following a pattern of felt dots on cardboard. Our cross-correlation analysis revealed reliable temporal synchronization between gaze-related signals and the instructed trajectory across all conditions. Statistical comparison tests and equivalence tests demonstrated that EOG tracking was statistically equivalent to the camera-based eye tracker gaze direction during the eyes-open condition. The camera-based eye-tracking glasses do not support tracking with closed eyes. Therefore, we evaluated the EOG-based gaze estimates during the eyes-closed trials by comparing them to the instructed trajectory. The results showed that EOG signals, guided by proprioceptive cues, followed the instructed path and achieved a significantly greater accuracy than shuffled control data, which represented a chance-level performance. This demonstrates the advantage of EOG when camera-based eye tracking is infeasible, and it paves the way for the development of eye-movement input interfaces for blind people, research on eye movement direction when the eyes are closed, and the early detection of diseases. Full article

An innovative, STEAM-based educational approach uses LEGO^® robots to improve the visualisation and understanding of trochoid curves in tertiary mathematics education. The method involves a two-step process: first, the curves are drawn based on the classical definition of trochoids using a custom-designed LEGO^® robot that employs LED light to trace the shapes. Then, the same process is replicated with a marker, with the robot controlling the movement of the drawing head to reproduce the curves accurately. To deepen students’ comprehension and visualisation, Desmos dynamic geometry software was used in parallel to draw all three types of trochoids (prolate, curtate, and cusped). This hands-on technique aims to make these motion curves more tangible and engaging within a classroom setting. A quantitative experiment involving 94 first-year IT BSc students was conducted during the spring semester of the 2024/2025 academic year using a quasi-experimental design. We had one control group and two experimental groups. One of the experimental groups did not use educational robotics; participants could only see how the robots worked via video. The other experimental group gained first-hand experience by building and testing LEGO^® drawing robots. The aim was to evaluate the effectiveness of an innovative teaching method that used educational robotics to improve understanding of the mathematical properties of trochoids, compared to traditional teaching methods and presentations containing short videos. The Mann–Whitney U test was used in all cases during hypothesis testing. Only watching videos of drawing robots does not have a statistically significant effect on learning outcomes. In this case, the effect size was only 0.12. However, the results of the group performing educational robotics activities showed a statistically significant difference compared to the other two groups, with large effect sizes (0.68 and 0.7). Our results suggest that visualisation using LEGO^® robots significantly improves students’ knowledge of parametric curves. Educational robotics offers promising opportunities because it is an attractive and interactive teaching tool. Its great advantage is that it combines abstract mathematical concepts with modern technology, thus creating an effective learning environment. Full article

Deploying autonomous vehicles in urban mobility systems promises significant improvements in safety, efficiency, and sustainability. On the other hand, running these vehicles in the continuously changing and often uncertain conditions of modern cities turns out to be a major challenge. These cars need advanced systems that can continuously change in order to observe conditions. This paper puts forward a new way that brings together multiple LIDAR sensors for the real-time spotting and following of objects, along with adaptive motion planning methods made to handle the difficulties of city traffic. Using LIDAR-based mapping for environmental modeling and predictive tracking techniques helps the system build a richly detailed, consistently updating depiction of surroundings that supports accurate and quick decisions. Another feature of the system is dynamic path planning that ensures safe navigation by considering traffic, pedestrian movement, and road conditions. Simulations carried out in highly dense urban scenarios show improvement in collision avoidance, path-planning optimization, and response to environmental dynamics. Such outcomes prove that combining multi-LIDAR tracking and adaptive motion planning contributes significantly to the performance and safety of an autonomous vehicle when operating in very complex urban conditions. Full article

Background/Objectives: Suprascapular nerve block (SSNB) is a useful therapeutic option for chronic shoulder pain, although the synergistic use of corticosteroids with anesthetics to prolong its effect is a controversial topic. The primary objective of this study was to compare the evolution of pain and functionality using the visual analog scale (VAS) and the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire between patients treated with SSNB with corticosteroids (cSSNB) and without them (sSSNB). Methods: A retrospective, observational, longitudinal, analytical cohort study was conducted in 28 patients (14 n per group) aged 50–80 years who had undergone SSNB with 4 mL of 0.25% bupivacaine and 40 mg/mL triamcinolone during 2024 for chronic shoulder pain lasting more than 6 months. The variables to be collected were VAS, DASH, range of motion (ROM) and Lattinen Index (LI) at baseline, the first and the third month. Patients were grouped according to the type of SSNB (cSSNB vs. sSSNB) and analyzed longitudinally and cross-sectionally using IBM-SPSS Statistics version 28.0.0. Results: Regarding pain, the cSSNB obtained a significant reduction in the median VAS of 4 points in the first month (p = 0.001) and in the third month (p = 0.002). In addition, significantly lower evaluations in VAS were obtained in the third month of 3 points (p = 0.04) in favor of the cSSNB. Regarding functionality, a reduction in evaluations with respect to the initial DASH were observed only in the cSSNB, with a difference in the first month of 21.80 points (p = 0.001) and 21.35 points (p = 0.003) in the third month. In addition, differences between groups were found, in favor of the cSSNB, of 19.20 points (p = 0.017) in the first month and 12.55 points (p = 0.012) in the third month. Conclusions: The combined use of corticosteroids in SSNB appears to be associated with better short-to medium-term outcomes in terms of pain and function, compared to the use of SSNB without corticosteroids in chronic rotator cuff pathologies. Full article

Stroke often leads to persistent upper limb dysfunction that impairs activities of daily living, yet objective biomechanical indicators for precise assessment remain limited. This study aimed to characterize phase-specific impairments in energy output, torque stability, and muscle coordination during the hand-to-mouth (HTM) task and to explore their potential for improving rehabilitation evaluation. Motion data from 20 stroke patients and 20 healthy controls were recorded using wearable surface electromyography and inertial measurement unit systems. A musculoskeletal model was applied to calculate joint torque, mechanical work, torque smoothness, and a novel torque-based co-contraction index across four movement subphases. These phase-specific metrics demonstrated significant correlations with clinical motor impairment scores, confirming their clinical validity. Significant dynamic features were then selected to construct machine learning models for group classification. Stroke patients showed reduced output capacity, increased torque fluctuations, and abnormal co-contraction patterns that varied across subphases. Among the classifiers, the quadratic support vector machine achieved the best performance, with an accuracy of 84.6% and an AUC of 0.853, surpassing models based on whole-task features. These findings demonstrate that phase-specific biomechanical features sensitively capture neuromuscular deficits in stroke survivors and highlight the potential of phase-specific biomechanics to inform future individualized rehabilitation assessment and treatment planning. Full article

Modern dental education increasingly calls for smarter tools that combine precision with meaningful feedback. In response, this study presents the Intelligent Dental Handpiece (IDH), a next-generation training tool designed to support dental students and professionals by providing real-time insights into their techniques. The IDH integrates motion sensors and a lightweight machine learning system to monitor and classify hand movements during practice sessions. The system classifies three motion states: Alert (10°–15° deviation), Lever Range (0°–10°), and Stop Range (>15°), based on IMU-derived features. A dataset collected from 61 practitioners was used to train and evaluate three machine learning models: Logistic Regression, Random Forest, Support Vector Machine (Linear RBF, Polynomial kernels), and a Neural Network. Performance across models ranged from 98.52% to 100% accuracy, with Random Forest and Logistic Regression achieving perfect classification and AUC scores of 1.00. Motion features such as Deviation, Take Time, and Device type were most influential in predicting skill levels. The IDH offers a practical and scalable solution for improving dexterity, safety, and confidence in dental training environments. Full article