Abstract Objectives To develop and evaluate a virtual-reality-enhanced lower-limb rehabilitation framework that combines EEG-based motor-intention detection, robotic gait assistance, trunk-motion assessment, and lumbar surface electromyography to support multimodal assessment, interactive training, and therapist-oriented performance monitoring. Methods Nine healthy participants completed VR-enhanced EEG motor-imagery training, open-loop calibration, closed-loop exoskeleton-triggering, trunk-attitude assessment, and lumbar-sEMG evaluation in separate experimental sessions. A four-degree-of-freedom hip–knee exoskeleton was integrated with participant-specific CDF threshold calibration, treadmill synchronization, VR rehabilitation tasks, attitude sensing, and RMS/MPF analysis. Participant identity was consistent across modalities, although the signals were not acquired simultaneously. Results The seated VR motor-imagery condition produced the highest open-loop command accuracy of 75.35 ± 4.21 %. Mean closed-loop gait-triggering accuracy was 76.7 ± 10.0 %, ranging from 60 % to 90 %. Mean trunk-movement magnitudes were 30.8°, 30.7°, 91.3°, and 31.0° for left lateral flexion, right lateral flexion, forward flexion, and backward extension, respectively. System prompting increased mean RMS from 0.094759 to 0.103330 mV and reduced mean MPF from 111.2023 to 103.0729 Hz. Conclusions The findings support the feasibility of integrating intention-driven exoskeleton control, VR interaction, postural assessment, and muscular-response monitoring within a unified rehabilitation framework for adaptive, multimodal rehabilitation.
Abstract Objectives This study compared the classification performance of VGG16 and MobileNetV2 models for normal-versus-lung-cancer-positive pulmonary CT image classification, with Grad-CAM used for post-hoc visual explainability. Methods A hospital-provided dataset of 550 non-contrast thoracic CT images, comprising 240 normal and 310 lung cancer-positive images, was divided into training, validation and test subsets using a patient-grouped stratified 50/10/40 split. Images were acquired at 120 kV with a 512 × 512 matrix using a NeuViz 128 CT scanner. Both models used the same preprocessing, training-only augmentation and evaluation protocol. The VGG16 model used stacked convolutional blocks, whereas MobileNetV2 used inverted residual bottlenecks with global average pooling, dropout and a sigmoid classification layer. Performance was assessed using accuracy, sensitivity, specificity, precision, F1-score, balanced accuracy, ROC-AUC, MCC and Cohen’s kappa, while Grad-CAM examined decision-relevant regions. Results On the 220-image independent test set, VGG16 achieved 0.9636 accuracy, 0.9677 sensitivity, 0.9583 specificity and 0.9569 ROC-AUC. MobileNetV2 achieved 0.9909 accuracy, 0.9919 sensitivity, 0.9896 specificity and 0.9858 ROC-AUC, with fewer false-positive and false-negative predictions. Conclusions MobileNetV2 achieved stronger held-out test performance than VGG16 under identical experimental conditions. Larger multi-centre patient-level validation remains necessary before clinical deployment in CT screening workflows.
Abstract Objectives Autism spectrum disorder (ASD) shows heterogeneous neurodevelopmental patterns across childhood, juvenile development, and adulthood, so binary MRI classification may not fully capture age-and sex-related variation. This study proposes a proof-of-concept framework for multiple classification using ABIDE-II T1-weighted structural MRI (sMRI). Methods After image-quality screening, sMRI slices were processed using Canny edge detection, brain-region cropping, 224 × 224 resizing, and augmentation. Images were organized into sex-based four-class, age-based four-class, and combined age-and-sex eight-class datasets, separating child, young, juvenile, adult, male, and female groups. A Batch-Normalized Grid-Optimized CNN (BN-GO-CNN) was developed to learn structural representations directly from preprocessed sMRI, with grid search used to select key CNN hyperparameters. The model was compared with VGG16, DenseNet201, MobileNetV2, and EfficientNet-B0 using identical image partitions. Results BN-GO-CNN achieved validation accuracies of 67.0 %, 64.0 %, and 60.5 % for sex-based, age-based, and combined age-and-sex classification, respectively. These outcomes remained above chance but showed limited class separability. Conclusions The modest performance suggests that label-related sMRI differences are subtle, supporting this framework as a preliminary test of CNN-assisted sMRI classification for future research use rather than a clinically reliable ASD diagnostic system.
Objectives: Diameter-based aortic assessment does not fully describe interactions among adult aortic remodeling, stroke-volume input, and regional flow organization. This paper aims to assess phase-contrast cardiac MRI of the aortic reservoir-flow coupling across different adult age groups. Methods: This single-center observational study included 252 adults undergoing thoracic aortic phase-contrast cardiac Magnetic Resonance Imaging (MRI) on a 3.0-T Siemens MAGNETOM Skyra scanner with an 18-channel abdominal phased-array coil. Participants were stratified as younger adults (18-33 years, n=66), middle-aged adults (34-60 years, n=119), and older adults (>60 years, n=67). Velocity-encoded imaging covered the thoracic aorta with TR 38.88 ms, TE 68.8 ms, flip angle 8 degrees, matrix 256 & times; 256, FOV 300 mm, spatial resolution 1.4 & times; 1.4 mm(2), slice thickness 3 mm, and interslice gap 0.6 mm. Centerline segmentation defined six analysis planes; aortic geometry, LV stroke volume, nSV, velocity, vorticity, and helicity were analyzed using group comparison, regression, and determinant modeling. Results: Older adults showed larger aortic volume (176.2 +/- 50.8 vs. 114.6 +/- 27.1 mL), ascending aortic diameter (33.8 +/- 5.8 vs. 28.3 +/- 3.5 mm), and lower nSV (0.44 +/- 0.15 vs. 0.92 +/- 0.18) than younger adults. Velocity and vorticity attenuated regionally, helicity approached zero distally, and nSV was the dominant determinant in later adulthood. Conclusions: Phase-contrast cardiac MRI identified a reservoir-flow coupling phenotype characterized by enlarged aortic reservoir volume, reduced relative stroke-volume input, and attenuated systolic flow organization.
