Hand-related impairments from stroke and neuromuscular diseases are rising globally, increasing the demand for prosthetic hands using surface electromyography (sEMG) for hand movement and grasp activities. sEMG systems also support human-robot interaction (HRI), aiding assistive robots in manipulator tasks. However, sEMG sensors often struggle to capture comprehensive muscle activity and are vulnerable to external hazards. In such scenarios, classical dimensionality reduction techniques often underperform, particularly when the intrinsic spatial structure of the data is overlooked. Addressing this limitation, local manifold-inspired learning techniques, such as locality preserving projection (LPP), are investigated in this study. In particular, orthogonal LPP (OLPP) helps extracting features from high-dimensional data by leveraging the orthogonal properties of nonlinear mappings from input to feature space. Nevertheless, traditional projection kernels are typically constructed based on Euclidean similarity between data points, making them highly sensitive to noise and outliers. To overcome this challenge, we have incorporated two additional similarity measures derived from the complex Euler space and Grassmannian manifold, which effectively explore the local structure in Riemannian spaces. A novel uncertainty-aware Bayesian model averaging (UaBMA) approach is proposed for integrating similarity weights from various manifolds, thereby enhancing the projection discriminability. Extensive experimental studies on both laboratory-acquired and benchmark NinaPro datasets demonstrate superior performance of the proposed technique over existing methods.

Drivers’ drowsiness has been considered one of the prime reasons for accidents and road fatalities. Drowsiness may be caused by sleep disorders resulting in unusual mental and health conditions that have detrimental effects on human lives. This article aims to present an attention deep learning (DL) framework for drivers’ drowsiness monitoring for an intelligent transportation system. The proposed imaging system, comprising an Infrared-Cut camera embedded in a microcomputer, has been employed for capturing both day and night mode images for automated detection of drivers’ drowsiness. The frames captured are preprocessed and fed to the proposed attention DL framework based on “you only look once” version 3 (YOLOv3) for eye region detection followed by eye state classification and interpretation. Feature extraction has been carried out via a convolutional neural network module, and multiscale fusion along with the non-maximum suppression method has been applied to detect and classify the eye region of the drivers for monitoring drowsiness. Moreover, the eye region has been interpreted via a classification activation map using the proposed attention module. Experimental evaluations reveal the efficacy of the proposed system on our acquired dataset and two benchmark datasets. The proposed drowsiness detection device and system can possess good potential by increasing safety in an advanced driver assistance system.

Hazy images suffer from poor visibility and possess low-contrast, degrading the visibility of the scene. The performance of single-image haze removal methods is limited by priors or constraints. This article presents an efficient single image dehazing method by cascading two models. The first model is a new atmospheric scattering model from which atmospheric light and transmission map are estimated and the second model is the proposed sparse haze model from which a haze map is estimated. A least-square optimization is carried out on the haze map to restore the haze-free image. Moreover, the analysis of the haze removal process has been investigated by a membership function to observe how much haze has been removed from the input hazy images containing various degrees of haze. Dehazing performances are evaluated on three types of datasets, i.e., real-world hazy images, synthetic images of various degrees of haze, and our developed dataset containing thick haze. Experimental results demonstrate that the proposed approach generates better performance than the state-of-the-art methods, especially in the sky or objects containing white objects, both qualitatively and quantitatively.

Computer-aided diagnosis have made a significant breakthrough in skin lesion diagnosis employing deep learning (DL) frameworks over the years, but it hardly reveals the transparency of the DL architecture. To mitigate this issue, in this article, we propose an image visibility filter (IVF) based DL framework for skin lesion diagnosis. The proposed IVF-DL network employs a ResNet architecture where visibility patches, extracted from the image visibility graph (IVG), are used as the convolutional kernels to extract salient features from dermoscopic images. The primary aim of this article is not only to classify skin lesions but also to depict the interpretable results after each residual block in a supervised manner. An optimal performance has been obtained by tuning three hyperparameters of the proposed method. Furthermore, the final interpretable result has been analyzed via IVG to resemble its spatial characteristics. Experimental results reveal that the proposed system outperforms the state-of-the-art classification methods quantitatively in terms of four performance metrics (accuracy, sensitivity, specificity, and area under the receiver operating curve) and qualitatively in terms of class activation map and relevance map considering two benchmark datasets.

Condition monitoring of road surfaces has acquired a lot of attention in the field of computer vision throughout the years. It is due to two main reasons; firstly, it produces safety and comfort to the community, and secondly, it causes less damage to the vehicles for an advanced driver assistance system (ADAS). To this extent, this article aims to present a real-time vision-based approach that automatically segments the road anomalies from the drivable area. An Intel RealSense D435 depth camera has been employed to capture RGB and depth (RGB-D) images of the road surface. An unsupervised learning method based on diffusion process has been employed to learn the affinity matrix of the RGB-D data and spectral clustering has been applied on the updated affinity matrix to cluster the road images. Image multiplex visibility graphs of the input sensor data are diffused by regularized diffusion process (RDP) to update the affinity matrix followed by generation of saliency map of the road surfaces. The prime motive to employ RDP is to use the graph Laplacian as a tool for similarity measurement for preserving the manifold structure. Qualitative and quantitative results reveal the efficacy of the proposed system with state-of-the-art methods on our RGB-D dataset. Benchmark datasets (KITTI and Cityscapes) are also used to validate the proposed method for segmentation of the drivable area for an intelligent transportation system.

This article presents a new convolutional neural network (CNN) topology using wavelet kernels to detect and discriminate single or multiple partial discharge (PD) locations in high voltage power apparatus with increased accuracy. The method is tested on an electrical equipment model with acoustic PD sensors. A cubical tank has been emulated in the laboratory representing the equipment under test and partial discharge sources have been placed at different positions along with the required data acquisition hardware. The present scheme eliminates the requirement of separate algorithms for feature extraction and classification of the acquired PD signals. Wavelet kernels of the CNN play a crucial role in feature learning, and the proposed CNN architecture as a whole, can classify the features in a supervised manner. The performance of the proposed scheme is compared with other existing methods using the same data set. It is found that an overall accuracy of 97.64% is achieved by the proposed method, outperforming other existing methods by a significant margin of at least 5% in terms of accuracy. The developed module is a generic one and can be adapted for different high voltage electrical apparatus with similar topological structures; hence, it can be used in various ways in power industry.

Abstract: In this paper Elman's recurrent neural network (ERNN) is employed for automatic identification of healthy and pathological gait and subsequent diagnosis of the neurological disorder in pathological gaits from the respective gait patterns. Stance, swing and double support intervals (expressed as percentages of stride) of 63 subjects were analysed for a period of approximately 300 s. The relevant gait features are extracted from cross‐correlograms of these signals with corresponding signals of a reference subject. These gait features are used to train modular ERNNs performing binary and tertiary classifications. The average accuracy of binary classifiers is obtained as 90.6%–97.8% and that of tertiary classifiers is 89.8%. Hence, two hierarchical schemes are developed each of which uses more than one modular ERNN to segregate healthy, Parkinson's disease, Huntington's disease and amyotrophic lateral sclerosis subjects. The average testing performances of the schemes are 83.8% and 87.1%.