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In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on…
Collaborative filtering has been largely used to advance modern recommender systems to predict user preference. A key component in collaborative filtering is representation learning, which aims to project users and items into a low…
Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic properties, such as their ability to work in low…
Daily life of thousands of individuals around the globe suffers due to physical or mental disability related to limb movement. The quality of life for such individuals can be made better by use of assistive applications and systems. In such…
Our hands reveal important information such as the pulsing of our veins which help us determine the blood pressure, tremors indicative of motor control, or neurodegenerative disorders such as Essential Tremor or Parkinson's disease. The…
Hand gestures have evolved into a natural and intuitive means of engaging with technology. The objective of this research is to develop a robust system that can accurately recognize and classify hand gestures representing numbers. The…
The classification of different fine hand movements from EEG signals represents a relevant research challenge, e.g., in brain-computer interface applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand…
The lack of interpretability of existing CNN-based hand detection methods makes it difficult to understand the rationale behind their predictions. In this paper, we propose a novel neural network model, which introduces interpretability…
Modeling and recognition of surgical activities poses an interesting research problem. Although a number of recent works studied automatic recognition of surgical activities, generalizability of these works across different tasks and…
Due to the universal non-verbal natural communication approach that allows for effective communication between humans, gesture recognition technology has been steadily developing over the previous few decades. Many different strategies have…
Skeleton-based action recognition is a central task in computer vision and human-robot interaction. However, most previous methods suffer from overlooking the explicit exploitation of the latent data distributions (i.e., the intra-class…
Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture…
Gesture recognition is a pivotal technology in the realm of intelligent education, and millimeter-wave (mmWave) signals possess advantages such as high resolution and strong penetration capability. This paper introduces a highly accurate…
Online continuous action recognition has emerged as a critical research area due to its practical implications in real-world applications, such as human-computer interaction, healthcare, and robotics. Among various modalities,…
Myoelectric control is one of the leading areas of research in the field of robotic prosthetics. We present our research in surface electromyography (sEMG) signal classification, where our simple and novel attention-based approach now leads…
In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank (C-HiSLR) model that is natural for recognizing human emotion in visual data. Previous attempts require explicit expression components, which are often unavailable…
Recognition of hand gestures is one of the most fundamental tasks in human-robot interaction. Sparse representation based methods have been widely used due to their efficiency and low demands on the training data. Recently, nonconvex…
This study mainly explores the application of natural gesture recognition based on computer vision in human-computer interaction, aiming to improve the fluency and naturalness of human-computer interaction through gesture recognition…
In this paper, we present an efficient method to incrementally learn to classify static hand gestures. This method allows users to teach a robot to recognize new symbols in an incremental manner. Contrary to other works which use special…
We propose a new framework for cooperative spectrum sensing in cognitive radio networks, that is based on a novel class of non-uniform samplers, called the event-triggered samplers, and sequential detection. In the proposed scheme, each…