Search Results (304)

Objectives: Frozen shoulder (FS) leads to pain, reduced shoulder function, and deficits in postural stability and sensorimotor control during upper-limb weight-bearing and activities of daily living tasks. This study investigated how an eight-week Dynamic Neuromuscular Stabilization (DNS) program affected Center of Pressure (COP) control and pain in midlife women with FS. Methods: Twenty-two midlife women with FS were randomly assigned to a DNS group (DNSG, n = 11) or a control group (CG, n = 11). The DNSG performed DNS exercises twice weekly for eight weeks, while the CG performed a dynamic stretching–based active control program. COP variables (distance, velocity, and root mean square (RMS) in the anterior–posterior (AP) and medial–lateral (ML) directions) were measured using a force platform under affected-side single-hand support with visual input and bilateral hand support with and without visual input. Pain was assessed using the Visual Analog Scale (VAS). All variables were analyzed using a two-way mixed ANOVA. Results: Under the affected-side single-hand support condition, a significant group × time interaction was observed for the prespecified primary outcome, ML-RMS (p < 0.05). Other COP variables under this condition were not significant after Holm–Bonferroni correction. Under bilateral hand-support conditions, ML-RMS remained significant after multiplicity adjustment in both visual conditions (p < 0.05). Pain (VAS) decreased over time in both groups, with no significant group × time interaction observed. Conclusions: The DNS intervention was associated with positive changes in COP-based postural control during upper-limb weight-bearing tasks in midlife women with FS. Pain decreased over time in both groups, with no significant group-by-time interaction. These findings suggest that DNS may be a potentially useful intervention for improving postural stability during upper-limb support tasks in patients with FS. Full article

Upper limb deficiencies can limit the range of tasks children can perform. Current prosthetics provide overall good performance to increase the activities that users can complete, but challenges remain. Body- or electrically powered prostheses struggle to restore the full range of motion needed for specific tasks. Currently, these systems do not allow for controlled hand closure or opening across all possible postures. A breathing-powered prototype named Airbender, which extracts energy from a breathing input by means of a Tesla turbine, provides the possibility of operation in any position. This paper introduces a novel design for a multi-finger actuated breathing-powered upper limb prosthetic concept and analyses its performance through a series of lab-based experiments. Results show that such a design could provide a fully controllable system. The final assembled design is capable of achieving full actuation under a flow rate of 340 Ls/min. The results obtained demonstrate that a functional multi-finger actuated breathing-powered upper limb prosthesis could be feasible and opens a path for future research in the field, with the ultimate goal of reducing the minimum flow rate required and actuation time to further improve its functionality. Full article

For garment manufacturing, an efficient and precise assessment of ergonomics is vital to prevent work-related musculoskeletal disorders. This study creates a computer vision-based algorithm for fast and accurate risk analysis. Specifically, we introduced SE and CBAM attention mechanisms into the YOLO network and integrated the optimized modules into the HRNet architecture to improve the accuracy of human pose recognition. This approach effectively addresses common interferences in garment production environments, such as fabric accumulation, equipment occlusion, and complex hand movements, while significantly enhancing the accuracy of human detection. On the COCO dataset, it increased mAP and recall by 4.43% and 5.99%, respectively, over YOLOv8. Furthermore, by analyzing key postural features from worker videos of cutting, sewing, and pressing, we achieved a quantified ergonomic risk assessment. Experimental results indicate that the RULA scores calculated using this algorithm are highly consistent and stable with expert evaluations and accurately reflect the dynamic changes in ergonomic risk levels across different processes. It is important to note that the validation was based on a pilot study involving a limited number of workers and task types, meaning that the findings primarily demonstrate feasibility rather than full-scale generalizability. Even so, the algorithm outperforms existing lightweight solutions and can be deployed in real-time on edge devices within factories, providing a low-cost ergonomic monitoring tool for the garment manufacturing industry. This helps prevent and reduce musculoskeletal injuries among workers. Full article

To investigate the characteristic pressure distribution patterns when gripping ski poles during skiing, this study addresses the challenges of measuring grip force on the complex curved surfaces of ski poles. A dataset of experimental samples was established, and grip force data were extracted using deep neural network (DNN) training. To reduce errors caused by dynamic force distribution and domain shifts due to varying hand postures, a hybrid method combining deep neural networks with the bio-inspired Gray Wolf Optimization (GWO) algorithm was proposed. This approach enables the fusion of hand-related feature data, facilitating the development of a high-precision grip force prediction model for skiing. A multi-point flexible array sensor was selected to detect force at key contact points. Through system calibration, grip force data were collected and used to construct a comprehensive database. A backpropagation (BP) neural network was then developed to process the sensor data at these characteristic points using deep learning techniques. The data fusion model was trained and further optimized through the GWO-BPNN (Gray Wolf Optimizer–backpropagation neural network) algorithm, which focuses on correcting and classifying force data based on dominant force-bearing units. Experimental results show that the optimized model achieves a relative error of less than 2% compared to calibration experiments, significantly improving the accuracy of flexible sensor applications. This model has been successfully applied to the development of intelligent skiing gloves, offering a scientific foundation for performance guidance and evaluation in skiing sports. Full article

In human daily-life scenarios, humanoid robots need not only to stand up smoothly but also to autonomously sit down for rest, energy management, and interaction. This capability is crucial for enhancing their autonomy and practicality. However, both sitting and standing involve complex dynamics constraints, diverse initial postures, and unstructured terrains, which make traditional hand-crafted controllers insufficient for multi-scenario demands. Reinforcement Learning (RL), with its generalization ability across high-dimensional state spaces and complex tasks, offers a promising solution for automatically generating motion control policies. Nevertheless, policies trained directly with RL often produce abrupt motions, making it difficult to balance smoothness and stability. To address these challenges, we propose a two-stage reinforcement learning framework: In the first stage, we focus on exploration and train initial policies for both sitting and standing, with relatively weak constraints on smoothness and joint safety, and without introducing noise. In the second stage, we refine the policies by tracking the motion trajectories obtained in the first stage, aiming for smoother transitions. We model the tracking problem as a bi-level optimization, where the tracking precision is dynamically adjusted based on the current tracking error, forming an adaptive curriculum mechanism. We apply this framework to a 1.7 m adult-scale humanoid robot, achieving stable execution in two representative real-world scenarios: sitting down onto a chair, stand up from a chair. Our approach provides a new perspective for the practical deployment of humanoid robots in real-world scenarios. Full article

Conductive hydrogels show great promise for wearable sensors but suffer from low sensitivity in small strain ranges. In this study, we developed a micropatterned composite hydrogel sheet (thickness: 1.2 ± 0.1 mm) by constructing a continuous electronic conductive network of carbon nanotubes (CNTs) on a highly crosslinked micropatterned hydrogel sheet. The sheet was fabricated via a two-step synthesis of a polyvinyl alcohol/polyacrylic acid polymer network—crosslinked by Zr⁴⁺ in a glycerol-water system—using sandpaper as the template. The first step ensured tight conformity to the template, while the second step preserved the micropattern’s integrity and precision. The reverse sandpaper micropattern enables secure bonding of CNTs to the hydrogel and induces localized stress concentration during stretching. This triggers controllable cracking in the conductive network, allowing the sensor to maintain high sensitivity even in small strain ranges. Consequently, the sensor exhibits ultra-high sensitivity, with gauge factors of 76.1 (0–30% strain) and 203.5 (30–100% strain), alongside a comfortable user experience. It can detect diverse activities, from subtle physiological signals and joint bending to complex hand gestures and athletic postures. Additionally, the micropatterned composite hydrogel sheet also demonstrates self-healing ability, adhesiveness, and conformability, while performing effectively under extreme temperatures and sweaty conditions. This innovative structure and sensing mechanism—leveraging stress concentration and controlled crack formation—provides a strategy for designing wearable electronics with enhanced performance. Full article

Background/Objectives: Although screening for scoliosis is common among adolescents, little is known about its subtypes and their correlations in young adults. This study aimed to investigate the prevalence and classification of scoliosis (functional vs. structural) among female university students in Saudi Arabia and to examine associated factors. Methods: A cross-sectional study was conducted with 263 female students at Taibah University. Screening was performed using Adam’s forward bending test and a scoliometer. Data on age, body mass index, academic year, hand dominance, habitual sitting posture, backpack carriage method, leg-length discrepancy, painful conditions, and family history of scoliosis were collected. The association between scoliosis subtypes and contributing factors was analyzed using descriptive statistics, chi-square test, and Spearman’s correlation. Results: Scoliosis was identified in 94 students (prevalence, 35.7%). Of these, 26.2% had functional scoliosis, whereas 9.5% had structural scoliosis. Functional scoliosis was significantly associated with sitting posture, leg-length discrepancy, and age (p < 0.05), whereas structural scoliosis was associated with family history, habitual sitting posture, and painful conditions (p < 0.05). Conclusions: The high prevalence and differing profiles of scoliosis subtypes underscore the need for routine postural screening in universities. Early identification and ergonomic education may help in selecting appropriate targeted interventions for individuals with functional or structural scoliosis. Full article

Precise localization of the fruit stem picking point is crucial for robots to achieve efficient harvesting operations. However, in unstructured orchard environments, citrus fruit stems are easily obscured by branches and leaves and affected by factors such as overlapping fruits. This leads to poor picking localization accuracy for robots, impacting their autonomous picking efficiency. Therefore, this paper proposes a method for estimating the posture of citrus fruit stems and performing picking operations under environmental occlusion, based on the YOLO-OBB algorithm. First, the YOLOv5s algorithm detects the ROI of citrus, combined with depth information to obtain their 3D point clouds. Second, the OBB algorithm constructs oriented point cloud bounding boxes to determine stem orientation and picking point locations. Finally, through hand–eye pose transformation of the robotic arm, the end-effector is controlled to achieve precise picking operations. Experimental results indicate that the average picking success rate of the YOLO-OBB algorithm reaches 82%, representing a 50% improvement over approaches without fruit stem estimation. This conclusively shows that the proposed algorithm provides precise fruit stem pose estimation, effectively enhancing robotic picking success rates under constrained fruit stem detection conditions. It offers crucial technical support for autonomous robotic harvesting operations. Full article

Reliable acupoint localization is essential for developing artificial intelligence (AI) and extended reality (XR) tools in traditional Korean medicine; however, conventional annotation of 2D images often suffers from inter- and intra-annotator variability. This study presents a low-cost dual-camera imaging system that fuses infrared (IR) and RGB views on a Raspberry Pi 5 platform, incorporating an IR ink pen in conjunction with a 780 nm emitter array to standardize point visibility. Among the tested marking materials, the IR ink showed the highest contrast and visibility under IR illumination, making it the most suitable for acupoint detection. Five feature detectors (SIFT, ORB, KAZE, AKAZE, and BRISK) were evaluated with two matchers (FLANN and BF) to construct representative homography pipelines. Comparative evaluations across multiple camera-to-surface distances revealed that KAZE + FLANN achieved the lowest mean 2D error (1.17 ± 0.70 px) and the lowest mean aspect-aware error (0.08 ± 0.05%) while remaining computationally feasible on the Raspberry Pi 5. In hand-image experiments across multiple postures, the dual-camera registration maintained a mean 2D error below ~3 px and a mean aspect-aware error below ~0.25%, confirming stable and reproducible performance. The proposed framework provides a practical foundation for generating high-quality acupoint datasets, supporting future AI-based localization, XR integration, and automated acupuncture-education systems. Full article

(1) Background: Hand and Arm Bimanual Intensive Therapy Including Lower Extremities (HABIT-ILE) is a high-dose evidence-based neurorehabilitation. This study aims to develop and validate a protocol using three inertial measurement units (IMUs) to objectively document upper and lower extremities’ (UE; LE) motor activity during a HABIT-ILE intervention in chronic stroke adults. (2) Method: Thirteen adults (57.1 y ± 11.3) who completed 65 h of HABIT-ILE (2 weeks, 6.5 h/d) were included. Daily motor activity was recorded with IMUs placed on both wrists and one thigh with nine IMU-derived variables extracted to evaluate motor activity and posture. Each variable was correlated with baseline abilities and day-to-day patterns were observed with subgroup analyses based on baseline dexterity and walking endurance. Day-to-day patterns were highlighted based on mean values and effect size analyses. (3) Results: Only the Magnitude and Use ratios showed high correlations with baseline abilities, with a day-to-day specific pattern highlighted for participants with moderate to good dexterity at baseline. (4) Conclusions: All participants reported a high level of engagement during HABIT-ILE independently of their baseline abilities. Although we could not detect a global trend to document the content of a HABIT-ILE intervention, these exploratory results suggest IMU monitoring to be relevant to characterize therapeutic content. Full article

This study presents a soft exoskeleton system designed to enhance the safety of electrical maintenance personnel during tower climbing by augmenting the hand grip and providing fall prevention assistance. Inspired by biological principles, a compact, stroke-amplified, and fast-response actuator based on a spring energy storage–release mechanism was developed and evaluated through tensile and speed tests, demonstrating sufficient locking force and a fast response time of 37.5 ms. A dual-sensing module integrating pressure and flexible bending sensors was designed to detect grasping states in real time. System effectiveness was further validated through functional electrical stimulation (FES) and simulated climbing experiments. FES tests confirmed the system’s ability to maintain grasp posture under involuntary hand extension, while climbing experiments verified consistent and reliable transitions between locking and unlocking during movement. Although preliminary, these results suggest that integrating soft exoskeletons with rapid-response actuators offers a promising solution for improving grip stability and operational safety in high-risk vertical environments. Full article

Background/Objectives: Idiopathic edema (IE) in women is characterized by swelling of hands and face followed by increasing abdominal and truncal girth, bloating, edema, >1.4 kg weight gain when in upright posture, and nocturia that eliminates the retained fluid. A capillary leak is the primary pathophysiologic abnormality that induces different clinical presentations that were considered untreatable. Methods: We utilized different aspects of Starling forces of edema formation and treated four uncomplicated cases of IE by reducing salt intake with or without diuretics and two cases of life-threatening cases due to seizures and coma induced by acute hyponatremia in one and postural dizziness, fainting, and fractures and dislocations of joints in another. Results: All four uncomplicated cases of IE were treated by reducing salt intake with or without diuretics that eliminated the weight gain and nocturia. The patient with hyponatremia never developed hyponatremia by reducing water intake and signs and symptoms of IE by salt restriction and diuretic therapy and eliminated the postural hypotension, falls, and fainting by use of support hose that increased interstitial hydrostatic pressure to eliminate fluid shifting from intravascular to interstitial spaces. Conclusions: A leaky capillary induces pathophysiologic changes that activate different metabolic pathways. IE is now a treatable condition, following: 1. Salt restriction with or without diuretics for the cyclical weight gain, and 2. Water restriction for hyponatremia, hyponatremic seizures, and coma and 3. support hose for postural hypotension, postural dizziness, and fainting. IE is unrecognized and probably more common than it is perceived. Full article

Background/Objectives: Children with DS are at an increased risk of obesity and impaired motor performance. This study aimed to compare body composition and motor performance in children with DS and typically developing (TD) peers and to explore associations between adiposity and motor function. Methods: A cross-sectional study was conducted with 42 children aged 6–12 years (23 DS, 19 TD). Body composition was assessed using bioelectrical impedance analysis. Postural stability was evaluated with the Pediatric Balance Scale; hand grip strength with hand dynamometry; physical activity with the PAQ-C; and aerobic endurance with the YMCA 3 min step test. Results: Children with DS exhibited significantly higher adiposity and lower motor performance than their TD peers. In the DS group, body fat mass was negatively correlated with physical activity (r = –0.499, p = 0.018), balance (r = –0.684, p < 0.001), and aerobic endurance (r = –0.389, p < 0.073). Regression analysis identified physical activity and fitness level as significant predictors of BMI in children with DS (R² = 0.825). Conclusions: Children with DS exhibit higher adiposity and inferior motor performance compared to their TD peers, with strong associations between adiposity and reduced physical activity, balance, and aerobic endurance. These findings underscore the importance of early targeted interventions to improve health outcomes in children with DS, particularly in regions like Saudi Arabia, where prevalence is high. Full article

Background: Dentists are at an increased risk of developing musculoskeletal disorders, particularly carpal tunnel syndrome (CTS), due to repetitive hand movements, awkward postures, and sustained grip forces. This study aimed to investigate the prevalence and risk factors of self-reported wrist and hand symptoms and clinically relevant CTS indicators among dentists in Jordan. Methods: A cross-sectional study was conducted among 201 licensed dentists in Jordan. Participants completed demographic questionnaires and the Valid Arabic version Boston Carpal Tunnel Questionnaire (BCTQ) to assess their symptom severity and hand function. Data was analyzed using STATA version 17, applying descriptive statistics, chi-squared tests, t-tests, ANOVA, and multivariable linear regression to evaluate association between sociodemographic factors and BCTQ scores. Results: Of the 201 participants, 64.2% were female and 35.8% were male. Female dentists were significantly younger (median age 31 vs. 39 years, p < 0.001), reported higher symptom severity (median score 18.0 vs. 16.0, p = 0.019), and experienced greater functional limitations (median score 15 vs. 9, p < 0.001) than male dentists. The overall mean symptom severity score was 19.12 (SD = 7.82), and the functional impairment score was 14.20 (SD = 6.37), indicating mild pain and functional limitation. Multivariable regression revealed that male sex was associated with significantly lower symptom scores (β = 0.7, p = 0.001) and better function (β = 0.722, p = 0.002). Geographic location, higher education level (PhD), and full-time employment were associated with higher symptom scores in the study. Conclusions: Wrist and hand symptoms are prevalent among dentists in Jordan, with significant sex differences in symptom severity and functional impairment. Geographic location, academic degree, and working hours were significant predictors of CTS-related symptoms. These findings underscore the need for ergonomic interventions and targeted preventive strategies, especially for high-risk groups, such as female and full-time practitioners. Full article

This study assessed the effects of passive arm-support exoskeletons (ASEs) on postural stability during repetitive arm-reaching tasks. In a 3 × 3 × 2 within-subject design, twenty-four healthy right-handed men completed left-, front-, and right-facing arm-reaching tasks at two working distances (65.5 and 68.9 cm) under three intervention conditions (Without, VEX, Airframe). Postural stability was assessed using center of pressure (CoP) data recorded from a force plate. Both ASEs clearly reduced the mean amplitude of CoP in the mediolateral (ML) direction (i.e., the absolute value of MEAN ML and ML APDF10), although neither yielded improvements in anteroposterior (AP) stability. Task direction significantly influenced all CoP measures: left-facing tasks produced the greatest leftward bias, whereas front-facing tasks yielded the smallest AP CoP amplitude. Increasing the working distance by <4 cm modestly heightened AP bias, as reflected in larger AP bias metrics (i.e., MEAN AP, ML APDF50, and ML APDF90). Overall, passive ASEs selectively enhanced lateral postural control, while their effect on AP stability was negligible or even slightly adverse. These findings indicate that the practical utility of passive ASEs depends on the directional demands of specific occupational tasks. Full article