Search Results (561)

Background/Objectives: Children with severe physical and communication disabilities face many challenges. They have very limited opportunities for upright, hands-free, self-initiated mobility. Current findings in neuroscience and theories on child development suggest that self-initiated mobility can have positive cascading effects on various developmental areas, including language and communication. This study was conducted to examine the current use of hands-free support walkers with children who have severe physical and communication disabilities and use augmentative and alternative communication and to identify the benefits and problems perceived by their parents and professionals from different disciplines. Methods: Online surveys were utilized to collect information from 127 participants, including 31 parents and 96 professionals or paraprofessionals. Results: The participants reported that these children could perform various motor activities in the hands-free support walkers to achieve different goals. Benefits identified by both parents and professionals included providing a way to exercise and stay active, improving motor control, enhancing independence, and bringing enjoyment. Professionals also observed positive impacts on communication, vocalization, use of eye contact, and problem solving. Conclusions: Results suggest that children with severe physical and communication disabilities can benefit from the upright, hands-free, self-initiated mobility provided by hands-free support walkers. Clinical implications and needs for future research are discussed. Full article

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies. Full article

Robotic timber joinery demands integrated, adaptive methods to compensate for the inherent dimensional variability of wood. We introduce a seamless robotic workflow to enhance the measurement accuracy of the Workpiece Coordinate System (WCS). The approach leverages a Zivid 3D camera mounted in an eye-in-hand configuration on a KUKA industrial robot. The proposed algorithm applies a geometric method that strategically crops the point cloud and fits planes to the workpiece surfaces to define a reference frame, calculate the corresponding transformation between coordinate systems, and measure the cross-section of the workpiece. This enables reliable toolpath generation by dynamically updating WCS and effectively accommodating real-world geometric deviations in timber components. The workflow includes camera-to-robot calibration, point cloud acquisition, robust detection of workpiece features, and precise alignment of the WCS. Experimental validation confirms that the proposed method is efficient and improves milling accuracy. By dynamically identifying the workpiece geometry, the system successfully addresses challenges posed by irregular timber shapes, resulting in higher accuracy for timber joints. This method contributes to advanced manufacturing strategies in robotic timber construction and supports the processing of diverse workpiece geometries, with potential applications in civil engineering for building construction through the precise fabrication of structural timber components. Full article

The mental and physical health of college students, especially in urban environments like Shanghai, is crucial given the high academic and urban stressors, which were intensified by the COVID-19 lockdown. Prior research has shown gender differences in health impacts during public health crises, with females often more vulnerable to mental health issues. Objective: This study aimed to comprehensively assess the physical and psychological health of Chinese college students post-lockdown, focusing on the relationship between stress, anxiety, depression, sleep patterns, and physical health, with a particular emphasis on gender differences. Methods: We conducted a cross-sectional study involving 116 students in Shanghai, utilizing psychological scales (HAMA, IPAQ, PSQI, SDS, FS 14, PSS, SF-36) and physical fitness tests (resting heart rate, blood pressure, hand grip, forced vital capacity, standing long jump, sit-and-reach, one-minute sit-up test and the one-minute squat test, single-leg stand test with eyes closed), to analyze health and behavior during the pandemic lockdown. All students have undergone the same life habits during the pandemic. Results: The HAMA scores indicated no significant levels of physical or mental anxiety. The PSS results (42.45 ± 8.93) reflected a high overall stress level. Furthermore, the PSQI scores (5.4 ± 2.91) suggested that the participants experienced mild insomnia. The IPAQ scores indicated higher levels of job-related activity (1261.49 ± 2144.58), transportation activity (1253.65 ± 987.57), walking intensity (1580.78 ± 1412.20), and moderate-intensity activity (1353.03 ± 1675.27) among college students following the lockdown. Hand grip strength (right) (p = 0.001), sit-and-reach test (p = 0.001), standing long jump (p = 0.001), and HAMA total score (p = 0.033) showed significant differences between males and females. Three principal components were identified in males: HAMA, FS14, and PSQI, explaining a total variance of 70.473%. Similarly, three principal components were extracted in females: HAMA, PSQI, and FS14, explaining a total variance of 69.100%. Conclusions: Our study underscores the complex interplay between physical activity (PA), mental health, and quality of life, emphasizing the need for gender-specific interventions. The persistent high stress, poor sleep quality, and reduced PA levels call for a reorganized teaching schedule to enhance student well-being without increasing academic pressure. Full article

This study explores the application of Eye-Tracking technology for the ergonomic evaluation of assistive device usability. Sixty-four participants evaluated six jar-opening devices in a two-phase study. First, the participants’ gaze was recorded while they viewed six rendered pictures of assistive devices, each shown in two different versions: with and without rubber in the grip area. Second, the participants physically interacted with the devices in a hands-on usability task. In both phases, participants rated the devices according to six usability affordances: robustness, comfort, easiness to grip, lid slippery, effort level, and easiness to use. Eye-Tracking metrics (fixation duration, number of fixations, and visit duration) correlated with the on-screen ratings, which aligned with ratings after using the physical devices. High ratings in comfort and effort level correlated with more visual attention to the grip area, where the rubber acted as key signifier. Heatmaps revealed the grip area as important for comfort and easiness to use and the lid area for robustness and slipperiness. These findings demonstrate the potential of Eye Tracking in usability studies, providing valuable insights for the ergonomic evaluation of assistive devices. Moreover, they highlight the suitability of Eye Tracking for early-stage design evaluation, offering objective metrics to guide design decisions and improve user experience. Full article

Large-sized components with numerous small key local features are essential in advanced manufacturing. Achieving high-precision quality control necessitates accurate and highly efficient three-dimensional (3D) measurement techniques. A flexible measurement system integrating a fringe-projection-based 3D scanner with an industrial robot is developed to enable the rapid measurement of large object surfaces. To enhance overall measurement accuracy, we propose an enhanced calibration method utilizing a multidimensional ball-based calibrator to simultaneously calibrate for hand-eye transformation and robot kinematic parameters. Firstly, a preliminary hand-eye calibration method is introduced to compensate for measurement errors at observation points, leveraging geometric-constraint-based optimization and a virtual single point derived via the barycentric calculation method. Subsequently, a distance-constrained calibration method is proposed to jointly estimate the hand-eye transformation and robot kinematic parameters, wherein a distance error model is constructed to link parameter errors with the measured deviations of a virtual single point. Finally, calibration and validation experiments were carried out, and the results indicate that the maximum and average measurement errors were reduced from 1.053 mm and 0.814 mm to 0.421 mm and 0.373 mm, respectively, thereby confirming the effectiveness of the proposed method. Full article

Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers—young (19–29 y) and middle-aged (35–56 y)—completed a VR Trail Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 × 2 × 2 mixed-model ANOVAs; post-trial scales captured usability (SUS), user experience (UEQ-S), and acceptability. Age dominated behaviour: younger adults were reliably faster, more precise, and less error-prone. Against this backdrop, input modality mattered. Eye-tracking yielded the best spatial accuracy and shortened Trail A time relative to manual control; head-gaze matched eye-tracking on Trail A speed and became the quickest, least error-prone option on Trail B. Controllers lagged on every metric. Subjective ratings were high across the board, with only a small usability dip in middle-aged low-gamers. Overall, gaze-based ray-casting clearly outperformed manual pointing, but optimal choice depended on task demands: eye-tracking maximised spatial precision, whereas head-gaze offered calibration-free enhanced speed and error-avoidance under heavier cognitive load. TMT-VR appears to be accurate, engaging, and ergonomically adaptable assessment, yet it requires age-specific–stratified norms. Full article

Background/Objective: This study aims to evaluate the predictive performance of three supervised machine learning algorithms—decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN) in forecasting key visual skills relevant to rhythmic gymnastics. Methods: A total of 383 rhythmic gymnasts aged 4 to 27 years were evaluated in various sports centers across Madrid, Spain. Visual assessments included clinical tests (near convergence point accommodative facility, reaction time, and hand–eye coordination) and eye-tracking tasks (fixation stability, saccades, smooth pursuits, and visual acuity) using the DIVE (Devices for an Integral Visual Examination) system. The dataset was split into training (70%) and testing (30%) subsets. Each algorithm was trained to classify visual performance, and predictive performance was assessed using accuracy and macro F1-score metrics. Results: The decision tree model demonstrated the highest performance, achieving an average accuracy of 92.79% and a macro F1-score of 0.9276. In comparison, the SVM and KNN models showed lower accuracies (71.17% and 78.38%, respectively) and greater difficulty in correctly classifying positive cases. Notably, the DT model outperformed the others in predicting fixation stability and accommodative facility, particularly in short-duration fixation tasks. Conclusion: The decision tree algorithm achieved the highest performance in predicting short-term fixation stability, but its effectiveness was limited in tasks involving accommodative facility, where other models such as SVM and KNN outperformed it in specific metrics. These findings support the integration of machine learning in sports vision screening and suggest that predictive modeling can inform individualized training and performance optimization in visually demanding sports such as rhythmic gymnastics. Full article

Cerebellar ataxias are a group of disorders characterized by clumsy movements because of defective muscle control. In affected individuals, muscular impairment might have an impact on activities like walking, balance, hand coordination, speech, and feeding, as well as eye movements. The development of symptoms typically takes place during the span of adolescence, and it has the potential to cause distress for individuals in many areas of their lives, including professional and interpersonal relationships. Although skeletal muscle is understudied in ataxias, its examination may provide hitherto unexplored details in this family of disorders. Observing muscle involvement can assist in diagnosing conditions where genetic tests alone are inconclusive. Furthermore, it helps determine the stage of progression of a pathology that might otherwise be challenging to assess. In this study, we reviewed the main scientific literature reporting on skeletal muscle examination in autosomal recessive cerebellar ataxias (ARCAs), with a focus on the rare Marinesco–Sjögren syndrome. (MSS). Our aim was to highlight the similarities in muscle alterations observed in ARCA patients while also considering data gathered from preclinical models. Analyzing the similarities among these disorders could enhance our understanding of the unidentified mechanisms underlying the phenotypic evolution of some less common conditions. Full article

To achieve robust and accurate collaborative 3D measurement under complex noise conditions, a global calibration method for dual-arm hand–eye systems and multi-view 3D imaging is proposed. A multi-view 3D scanning approach based on ICP (M3DHE-ICP) integrates a multi-frequency heterodyne coding phase solution with ICP optimization, effectively correcting stitching errors caused by robotic arm attitude drift. After correction, the average 3D imaging error is 0.082 mm, reduced by 0.330 mm. A global calibration method based on encoded marker points (GCM-DHE) is also introduced. By leveraging spatial geometry constraints and a dynamic tracking model of marker points, the transformation between multi-coordinate systems of the dual arms is robustly solved. This reduces the average imaging error to 0.100 mm, 0.456 mm lower than that of traditional circular calibration plate methods. In actual engineering measurements, the average error for scanning a vehicle’s front mudguard is 0.085 mm, with a standard deviation of 0.018 mm. These methods demonstrate significant value for intelligent manufacturing and multi-robot collaborative measurement. Full article

The ability to autonomously generate up-to-date 3D models of robotic workcells is critical for advancing smart manufacturing, yet existing Next-Best-View (NBV) methods often rely on paradigms ill-suited for the fixed-base manipulators found in dynamic industrial environments. To address this gap, this paper proposes a novel NBV method for the complete exploration of a 6-DOF robotic arm’s workspace. Our approach integrates collision-based information gain metric, a potential field technique to generate candidate views from exploration frontiers, and a tunable fitness function to balance information gain with motion cost. The method was rigorously tested in three simulated scenarios and validated on a physical industrial robot. Results demonstrate that our approach successfully maps the majority of the workspace in all setups, with a balanced weighting strategy proving most effective for combining exploration speed and path efficiency, a finding confirmed in the real-world experiment. We conclude that our method provides a practical and robust solution for autonomous workspace mapping, offering a flexible, training-free approach that advances the state-of-the-art for on-demand 3D model generation in industrial robotics. Full article

The demeanour of a human during an interaction with an animal may influence the animal’s emotional response. We investigated whether the emotional responses of laying hens to a threatening or neutral human and a novel environment were lateralised, from which their emotional state can be inferred. Twenty-five DeKalb white laying hens reared in furnished cages under environmentally controlled conditions were individually assessed for their responses to these stimuli. They were contained in a box before emerging into an arena with a threatening human, who attempted direct eye contact with the bird and had their hands raised towards it, or a neutral person, who had no eye contact and sat with their hands on their knees. When initially placed in the box adjacent to the test arena, birds that remained in the box used their left eye more than their right eye, and they showed evidence of nervousness, with many head changes, neck stretching, and vocalisation. Birds showed lateralised behaviour in both the box and arena. Birds entering the arena with the threatening person used their left eye (connected to the right brain hemisphere) more than their right eye, usually with their body less vertical, and were more likely to be standing than sitting, compared with those viewing the neutral person. This confirms the bird’s interpretation of the person as threatening, with left eye/right brain hemisphere processing of flight or fight situations. We conclude that lateralised responses of chickens suggest that a threatening person is viewed more fearfully than a neutral person. However, further investigation is required with a larger sample of birds to strengthen these findings and enhance the generalisability of behavioural responses. Full article

As the prevalence of related diseases continues to rise, a corresponding increase in the demand for internal limiting membrane (ILM) peeling surgery has been observed. However, significant challenges are encountered in ILM peeling surgery, including limited force feedback, inadequate depth perception, and surgeon hand tremors. Research on fully autonomous ILM peeling surgical robots has been conducted to address the imbalance between medical resource availability and patient demand while enhancing surgical safety. An automatic control framework for break initiation in ILM peeling is proposed in this study, which integrates model-predictive control with fractional-order admittance control. Additionally, a multi-vision task surgical scene perception method is introduced based on target detection, key point recognition, and sparse binocular matching. A surgical trajectory planning strategy for break initiation in ILM peeling aligned with operative specifications is proposed. Finally, validation experiments for automatic break initiation in ILM peeling were performed using eye phantoms. The results indicated that the positional error of the micro-forceps tip remained within 40 μm. At the same time, the contact force overshoot was limited to under 6%, thereby ensuring both the effectiveness and safety of break initiation during ILM peeling. Full article

Background: The ageing process is associated with a decline in cognitive functions, including eye–hand coordination, attention concentration, and psychomotor reaction time. This study aimed to assess the effectiveness of virtual reality–based therapy in enhancing cognitive functions in seniors. Methods: This study was conducted on 38 cases (29 women and 9 men) with a mean age of 87.2 years, who were divided into two groups: a VR group (with a 4-week, three-time-week training program using the game “Beat Saber”) and a control group (with a standard 4-week exercise program). Assessments of eye–hand coordination and attentional concentration were conducted at the beginning (T₀) and the end (T₁) of the training. Results: Analysis of eye–hand coordination and attentional concentration showed significant improvement in both groups (T₀ vs. T₁: p = 0.0002 for the intervention group and p = 0.007 for the control group). However, the effect in the VR group was almost three times greater than in the control group (1.689 vs. 0.615 in D effect). Moreover, in the VR group, an analysis of “good cuts” indicated improvements in both parameters after 4 weeks of VR training. The percentage of correctly received stimuli increased significantly across sessions (p < 0.00001). Furthermore, 84.3% of participants experienced a twofold improvement in performance over the 12 VR sessions (42% vs. 80% accuracy in successful hits). The distribution of results also suggests a positive subjective impact of VR therapy in maintaining mental activity. Conclusions: The findings indicate that VR-related training can support elderly individuals in recovering cognitive function, potentially enhancing their independence and life quality. Full article

This paper presents the implementation of a vision system for a collaborative robot equipped with a web camera and a Python-based control algorithm for automated object-sorting tasks. The vision system aims to detect, classify, and manipulate objects within the robot’s workspace using only 2D camera images. The vision system was integrated with the Universal Robots UR5 cobot and designed for object sorting based on shape recognition. The software stack includes OpenCV for image processing, NumPy for numerical operations, and scikit-learn for multilayer perceptron (MLP) models. The paper outlines the calibration process, including lens distortion correction and camera-to-robot calibration in a hand-in-eye configuration to establish the spatial relationship between the camera and the cobot. Object localization relied on a virtual plane aligned with the robot’s workspace. Object classification was conducted using contour similarity with Hu moments, SIFT-based descriptors with FLANN matching, and MLP-based neural models trained on preprocessed images. Conducted performance evaluations encompassed accuracy metrics for used identification methods (MLP classifier, contour similarity, and feature descriptor matching) and the effectiveness of the vision system in controlling the cobot for sorting tasks. The evaluation focused on classification accuracy and sorting effectiveness, using sensitivity, specificity, precision, accuracy, and F1-score metrics. Results showed that neural network-based methods outperformed traditional methods in all categories, concurrently offering more straightforward implementation. Full article