In the field of 3D skeleton action recognition, research on self-supervised learning methods has primarily focused on spatio-temporal feature modeling. However, these methods rely heavily on modeling single motion features, which limits their ability...
This paper proposes a lightweight video action recognition framework that integrates 3D Convolutional Neural Networks (CNNs), the Histogram Transformer Block (HTB), and the Split-Attention Residual Block (SAB), while also introducing Spatiotemporal T...
Optical Character Recognition (OCR) is a part of transformative Artificial Intelligence (AI) technology which translates printed or handwritten texts into digital, machine-readable form. These OCR systems act as an assistive tool for visually impaire...
Real-time and accurate hand gesture detection is essential for safe and intuitive Human-Robot Interaction (HRI), enabling robots to interpret non-verbal cues and respond appropriately in dynamic environments. This research evaluates the effectiveness...
The challenge of traffic sign detection and recognition for driving vehicles has become more critical with recent advances in autonomous and assisted driving technologies. Although object recognition problems, particularly traffic sign recognition, h...
In this paper, we propose a novel neural network architecture, the convolutional spider neural network (CS-Net), combined with a transfer learning (TL) strategy, to classify hybrid gestures that integrate wrist postures and hand movements.The CS-Net ...
In recent years, Hand Gesture Recognition (HGR) devices have been designed to recognize gestures in real time using machine-learning classifiers (MLCs). However, the performance of these classifiers heavily relies on the tuning of their hyperparamete...
Advanced large-scale neural interfaces call for efficient algorithms to automatically process and optimally exploit the richness of their heavy continuous flow of data. In this context, we introduce here a very frugal generic single-layer Spiking Neu...
Biomedical physics & engineering express
Oct 3, 2025
This paper proposes a novel gesture prediction method for accurately predicting hand gesture types from raw sEMG signals in real time. First, we utilize a linear combination of the mean and standard deviation of sEMG signals within a sliding window t...
Rapid urbanization and growing traffic volumes have increased the demand for efficient and accurate road damage detection to ensure traffic safety and optimize maintenance. Traditional manual and vehicle-mounted inspection methods are often inefficie...
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