Search Results (128)

Background: Gestures or manual signing are valid options for augmentative and alternative communication. However, the data in the literature are limited to a few neurodevelopmental disorders, and less is known about its application in the community setting. Objectives: This case report explores the feasibility and preliminary efficacy of tact training with augmentative gestural support intervention for a child affected by a language disorder with challenging behaviors in a community setting. Methods: Baseline assessments were conducted using the Verbal Behavior Milestone Assessment and Placement Program (VB-MAPP) and Griffiths Mental Developmental Scale-III (GMDS-III). The patient received six months of standard treatment, consisting of neuropsychomotor and speech therapy each twice a week, with improved cooperation in proposed activities, but no improvement in language. Afterward, a total of 24 sessions of tact training with augmentative gestural support interventions were performed. Data were collected by two independent observers and analyzed to measure language and behavioral outcomes. Results: VB-MAPP scores increased form minimal communication and social interaction at T0 (baseline) to improved compliance but unchanged language skills at T1 (after standard therapy). After tack training with augmentative gestural support (T2), VB-MAPP scores showed significant improvements, with notable increases in verbal operants, independence in communication, and intersubjectivity skills. GMDS-III scores at T2 also demonstrated growth in social, communicative, and cognitive skills. Additionally, challenging behaviors were reduced by more than 70% and nearly resolved by the end of the intervention. Conclusions: Personalized approaches appear to be essential for interventions tailored to developmental age. Further research is needed to determine the effectiveness of these approaches for other neurodevelopmental disorders, identify patient characteristics that may be predictors of outcomes to tailor the intervention, and explore the generalization of the results obtained with these strategies. Full article

Advancements in neural network approaches have enhanced the effectiveness of surface Electromyography (sEMG)-based hand gesture recognition when measuring muscle activity. However, current deep learning architectures struggle to achieve good generalization and robustness, often demanding significant computational resources. The goal of this paper was to develop a robust model that can quickly adapt to new users using Transfer Learning. We propose a Multi-Scale Convolutional Neural Network (MSCNN), pre-trained with various strategies to improve inter-subject generalization. These strategies include domain adaptation with a gradient-reversal layer and self-supervision using triplet margin loss. We evaluated these approaches on several benchmark datasets, specifically the NinaPro databases. This study also compared two different Transfer Learning frameworks designed for user-dependent fine-tuning. The second Transfer Learning framework achieved a 97% F1 Score across 14 classes with an average of 1.40 epochs, suggesting potential for on-site model retraining in cases of performance degradation over time. The findings highlight the effectiveness of Transfer Learning in creating adaptive, user-specific models for sEMG-based prosthetic hands. Moreover, the study examined the impacts of rectification and window length, with a focus on real-time accessible normalizing techniques, suggesting significant improvements in usability and performance. Full article

Asymmetries in sports are common and can lead to various issues; however, different training programs can facilitate change. This study aimed to assess the effects of opposing plyometric programs on tensiomyography lateral symmetry (TMG LS)/inter-limb asymmetry in female athletes’ lower-body muscles, alongside kinematic and body composition parameters. Twenty female subjects from basketball, volleyball, and track and field (sprinting disciplines) were divided into two experimental groups (n = 10 each). Two six-week plyometric programs (two sessions/week) were implemented: the first program (E1) focused on eccentric exercises, depth landings, while the second (E2) emphasized concentric exercises, squat jumps. TMG assessed LS in six muscles: vastus lateralis, vastus medialis, biceps femoris, semitendinosus, gastrocnemius lateralis, and gastrocnemius medialis. A kinematic analysis of the countermovement jump (CMJ) and body composition was conducted using “Kinovea; Version 0.9.4” software and InBody 770, respectively. The results showed significant increases in LS percentages (E1—VL 9.9%, BF 18.0%, GM 10.6% and E2—BF 22.5%, p < 0.05), and a significant large effect in E1 for VL, and in E2 for BF, p < 0.01). They also showed that E1 had a significant effect on VL, and that E2 had a significant large effect on BF (p < 0.01). E1 also led to increased lean muscle mass in both legs (left: 1.88%, right: 2.74%) and decreased BMIs (−0.4, p < 0.05). Both programs improved LS, with E1 enhancing muscle mass and lower-body positioning in CMJ. We recommend future studies use varied jump tests, incorporate 3D kinematic analysis, include male subjects, and examine more muscles to enhance TMG LS analysis. Full article

Accurate measurement of pedaling kinetics and kinematics is vital for optimizing rehabilitation, exercise training, and understanding musculoskeletal biomechanics. Pedal reaction force, the main external force in cycling, is essential for musculoskeletal modeling and closely correlates with lower-limb muscle activity and joint reaction forces. However, sensor instrumentation like 3-axis pedal force sensors is costly and requires extensive postprocessing. Recent advancements in machine learning (ML), particularly neural network (NN) models, provide promising solutions for kinetic analyses. In this study, an NN model was developed to predict radial and mediolateral forces, providing a low-cost solution to study pedaling biomechanics with stationary cycling ergometers. Fifteen healthy individuals performed a 2 min pedaling task at two different self-selected (58 ± 5 RPM) and higher (72 ± 7 RPM) cadences. Pedal forces were recorded using a 3-axis force system. The dataset included pedal force, crank angle, cadence, power, and participants’ weight and height. The NN model achieved an inter-subject normalized root mean square error (nRMSE) of 0.15 ± 0.02 and 0.26 ± 0.05 for radial and mediolateral forces at high cadence, respectively, and 0.20 ± 0.04 and 0.22 ± 0.04 at self-selected cadence. The NN model’s low computational time suits real-time pedal force predictions, matching the accuracy of previous ML algorithms for estimating ground reaction forces in gait. Full article

The joint moment is a key measurement in locomotion analysis. Transferable prediction across different subjects is advantageous for calibration-free, practical clinical applications. However, even for similar gait motions, intersubject variance presents a significant challenge in maintaining reliable prediction performance. The optimal deep learning models for ankle moment prediction during dynamic gait motions remain underexplored for both intrasubject and intersubject usage. This study evaluates the feasibility of different deep-learning models for estimating ankle moments using sEMG data to find an optimal intrasubject model against the inverse dynamic approach. We verified and compared the performance of 1302 intrasubject models per subject on 597 steps from seven subjects using various architectures and feature sets. The best-performing intrasubject models were recurrent convolutional neural networks trained using signal energy features. They were then transferred to realize intersubject ankle moment estimation. Full article

Constructed response items that require the student to give more detailed and elaborate responses are widely applied in large-scale assessments. However, the hand-craft scoring with a rubric for massive responses is labor-intensive and impractical due to rater subjectivity and answer variability. The automatic response coding method, such as the automatic scoring of short answers, has become a critical component of the learning and assessment system. In this paper, we propose an interactive coding system called ASSIST to efficiently score student responses with expert knowledge and then generate an automatic score classifier. First, the ungraded responses are clustered to generate specific codes, representative responses, and indicator words. The constraint set based on feedback from experts is taken as training data in metric learning to compensate for machine bias. Meanwhile, the classifier from responses to code is trained according to the clustering results. Second, the experts review each coded cluster with the representative responses and indicator words to score a rating. The coded cluster and score pairs will be validated to ensure inter-rater reliability. Finally, the classifier is available for scoring a new response with out-of-distribution detection, which is based on the similarity between response representation and class proxy, i.e., the weight of class in the last linear layer of the classifier. The originality of the system developed stems from the interactive response clustering procedure, which involves expert feedback and an adaptive automatic classifier that can identify new response classes. The proposed system is evaluated on our real-world assessment dataset. The results of the experiments demonstrate the effectiveness of the proposed system in saving human effort and improving scoring performance. The average improvements in clustering quality and scoring accuracy are 14.48% and 18.94%, respectively. Additionally, we reported the inter-rater reliability, out-of-distribution rate, and cluster statistics, before and after interaction. Full article

Challenges and tensions that arise in a pluralistic society with differing worldviews among its citizens must be addressed from the outset in school education. To enable social cohesion within a heterogeneous society, students must learn to harmonize their own worldviews with other interpretations of the world in a spirit of “reciprocal inclusivity” (Reinhold Bernhardt). This article argues that this task particularly falls within the responsibility of subjects in schools that address the existential “problems of constitutive rationality” (Jürgen Baumert), specifically religious education, ethics, and philosophy. In Germany and Austria, multiple subjects within denominational religious education, as well as ethics and philosophy, are offered in schools. When these subjects collaborate on projects, students learn to engage in dialogue with the various religious and secular, individual, and collective interpretations, perspectives, and worldviews they encounter. Since 2002/03, and in teacher training since 2011, such a didactically guided Sharing Worldviews approach has been implemented in school projects in Southern Germany through a four-phase concept. This concept can be flexibly applied to the local conditions of the school, contributes to internationalisation and digitalisation, and does not require additional teaching hours. By incorporating secular worldviews, Sharing Worldviews goes beyond interreligious learning and has also been realised digitally in other European countries. The following article begins by considering the educational requirements in a heterogeneous society (1), describes the prerequisites needed to positively influence students’ attitudes (2), outlines common foundational concepts for interreligious and inter-worldview dialogue (3), and recommends “Mutual Hospitality” as the basis for such dialogue in schools (4). The article then explains how “Mutual Hospitality” can be practically implemented in a four-phase concept of Sharing Worldviews both in schools and in teacher training (5 and 6) by tracing the origins of this concept (7). The Sharing Worldviews concept has been both internationalised and digitalised in schools and teacher education (8), aligns with the educational principles of the OECD (9), and demonstrates significant benefits in empirical studies (10). Full article

The InterCriteria decision-making approach, known as InterCriteria analysis (ICrA), was applied here to optimize the experimental protocol when the surface electromyography (EMG) signals of upper arm human muscles are recorded. Ten healthy subjects performed cycling movements in the sagittal plane with and without added weight for ten, six, two, and one second, respectively, for each active phase. The EMG signals from six muscles or parts of muscles, namely m. deltoideus pars clavicularis and pars spinata, m. brachialis, m. anconeus, m. biceps brachii, and m. triceps brachii caput longum, were recorded. ICrA was used on the obtained data to find correlations between the sixteen different phases, eight for elbow flexion and eight for elbow extension. Based on the obtained results, we proposed an optimized experimental protocol (OEP) that omits slower and more difficult tasks while saving crucial data. The optimized protocol consists of seven, instead of ten, tasks and takes three minutes less than the time taken for the full experimental protocol (FEP). The lower number of movements in the OEP could prevent physical and psychical fatigue, discomfort, or even pain in the investigated subjects. In addition, the time to train subjects, as well as the time to process the surface EMG data, can be significantly reduced. Full article

Autism Spectrum Disorder (ASD) presents significant diagnostic challenges due to its complex, heterogeneous nature. This study explores a novel approach to enhance the accuracy and reliability of ASD diagnosis by integrating resting-state functional magnetic resonance imaging with demographic data (age, gender, and IQ). This study is based on improving the spectral graph convolutional neural network (GCN). It introduces a multi-view attention fusion module to extract useful information from different views. The graph’s edges are informed by demographic data, wherein an edge-building network computes weights grounded in demographic information, thereby bolstering inter-subject correlation. To tackle the challenges of oversmoothing and neighborhood explosion inherent in deep GCNs, this study introduces DropEdge regularization and residual connections, thus augmenting feature diversity and model generalization. The proposed method is trained and evaluated on the ABIDE-I and ABIDE-II datasets. The experimental results underscore the potential of integrating multi-view and multimodal data to advance the diagnostic capabilities of GCNs for ASD. Full article

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p < 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings. Full article

Interlocking Inter-Module Connections (IMCs) in Modular Steel Buildings (MSBs) have garnered significant interest from researchers. Despite this, the optimisation of plate thicknesses in such structures has yet to be extensively explored in the existing literature. Therefore, this paper focuses on optimising the thickness of interlocking IMCs in MSBs by leveraging established experimental and numerical simulation methodologies. The study developed various numerical models for IMCs with plate thicknesses of 4 mm, 6 mm, 10 mm, and 12 mm, all subjected to compression loading conditions. The novelty of this study lies in its comprehensive parametric analysis, which evaluates the slip prediction model. A random forest regression model, trained using the ‘TreeBagger’ function, was also implemented to predict slip values based on applied force. Sensitivity analysis and comparisons with alternative methods underscored the reliability and applicability of the findings. The results indicate that a plate thickness of 11.03 mm is optimal for interlocking IMCs in MSBs, achieving up to 8.08% in material cost reductions while increasing deformation resistance by up to 50.75%. The ‘TreeBagger’ random forest regression significantly enhanced slip prediction accuracy by up to 7% at higher force levels. Full article

Medical education acknowledges our need to teach our physicians about “social determinants of health” and “health care disparities”. However, educators often lack actionable training to address this need. We describe a faculty development activity, a health disparities journal club, using the jigsaw strategy with the intent of increasing awareness, encouraging self-directed learning, and inspiring future teaching of the subject to health professional learners. We completed six workshops at six individual hospitals, with 95 total attendees in medicine and numerous other health professions. Our evaluation asked trainees to: report the number of journal articles about health disparities they had read, excluding the assigned journal club articles, in the past 12 months, and to predict future plans for reading about health disparities. In total, 28.9% responded they had “never read” a prior article on health or healthcare disparities, while 54.2% responded “1–5 articles”. Many (60%) reported they would continue to investigate this topic. Our experience has demonstrated the utility and positive impact of a “flipped classroom” jigsaw method, showing it can be used successfully in Inter-Professional (IPE) Faculty Development to increase active exposure and discussion of the content. Additionally, this method promotes individual reflection and enhances continued collective engagement. Full article

The present study focuses on the impact of exterior traffic noises on sound environment evaluation in office spaces, considering their interaction with interior noises. There were three interior noise conditions: silence, air-conditioner noise, and irrelevant speech noise. Six exterior traffic noises (road, maglev, tram, metro, conventional inter-city train, and high-speed train) were merged with interior noise clips to create the combined noise stimuli. Forty subjects participated in the experiment to assess the acoustic environment in office spaces exposed to multiple noises. The results showed that both interior and exterior noise significantly affected acoustic comfort and noise disturbance. As for the exterior traffic noise, both the traffic noise source and the noise level were found to be influential on both attributes. More temporally fluctuating traffic noises, such as high-speed train noise, were found to have a greater negative effect on subjective evaluations. Meanwhile, the interior noise source was also found to influence evaluations of the sound environment. Compared to the single traffic noise condition, irrelevant speech noise significantly increased the negative impact of traffic noises, while the air-conditioner noise had a neutral effect. In addition, participants in offices with speech noise were less sensitive to the traffic noise level. Full article

In recent years, the rapid prevalence of high-definition video in Internet of Things (IoT) systems has been directly facilitated by advances in imaging sensor technology. To adapt to limited uplink bandwidth, most media platforms opt to compress videos to bitrate streams for transmission. However, this compression often leads to significant texture loss and artifacts, which severely degrade the Quality of Experience (QoE). We propose a latent feature diffusion model (LFDM) for compressed video quality enhancement, which comprises a compact edge latent feature prior network (ELPN) and a conditional noise prediction network (CNPN). Specifically, we first pre-train ELPNet to construct a latent feature space that captures rich detail information for representing sharpness latent variables. Second, we incorporate these latent variables into the prediction network to iteratively guide the generation direction, thus resolving the problem that the direct application of diffusion models to temporal prediction disrupts inter-frame dependencies, thereby completing the modeling of temporal correlations. Lastly, we innovatively develop a Grouped Domain Fusion module that effectively addresses the challenges of diffusion distortion caused by naive cross-domain information fusion. Comparative experiments on the MFQEv2 benchmark validate our algorithm’s superior performance in terms of both objective and subjective metrics. By integrating with codecs and image sensors, our method can provide higher video quality. Full article

In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607. Full article