Md Atiqur Rahman Ahad 교수

In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain-computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalo

Action and activity representation and recognition are very demanding research area in computer vision and man-machine interaction. Though plenty of researches have been done in this arena, the field is still immature. Over the last decades, extensive research methodologies have been developed on human activity analysis and recognition for various applications. This paper overviews various recent methods for human activity recognition with analysis. We attempt to sum up the various methods relat

Action recognition is a very widely explored research area in computer vision and related fields. We propose Kinematics Posture Feature (KPF) extraction from 3D joint positions based on skeleton data for improving the performance of action recognition. In this approach, we consider the skeleton 3D joints as kinematics sensors. We propose Linear Joint Position Feature (LJPF) and Angular Joint Position Feature (AJPF) based on 3D linear joint positions and angles between bone segments. We then comb

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this c

The book is about computer vision and action recognition & understanding. If you have a basic knowledge on image processing and would like to study or to do research on action/activity understanding and recognition in computer vision and related arenasthen this is the book for you! Throughout my endeavor to write this book, I have undergone many discussions with many experts during various conferences and academic meetings, discussed over emails on many challenging issues. I would like to thank

Hand gesture recognition is one of the most widely explored areas under the human-computer interaction domain. Although various modalities of hand gesture recognition have been explored in the last three decades, in recent years, due to the availability of hardware and deep learning algorithms, hand gesture recognition research has attained renewed momentum. In this paper, we evaluate the effectiveness of a low-cost dataglove for classifying hand gestures in the light of deep learning. We have d

Quadruped robots can mimic animal walking gait and they have certain advantages like walking on terrain and extremely rough surfaces. Obstacles can impede the movement of wheeled vehicles, where a quadruped can adapt to avoid obstacles by adjusting its height. A quadruped robot is designed and developed for in this paper, which could be controlled by the Android operating system. The Inverse Kinematics Solutions are derived for the developed structure using Denavit-Hartenberg convention and usin

Human action analyses and recognition are challenging problems due to large variations in human motion and appearance, camera viewpoint and environment settings. The field of action and activity representation and recognition is relatively old, yet not well-understood by the students and research community. Some important but common motion recognition problems are even now unsolved properly by the computer vision community. However, in the last decade, a number of good approaches are proposed an

In this paper, we develop a real-time mobile phone-based gaze tracking and eye-blink detection system on Android platform. Our eye-blink detection scheme is developed based on the time difference between two open eye states. We develop our system by finding the greatest circle – pupil of an eye. So we combine the both Haar classifier and Normalized Summation of Square of Difference template matching method. We define the eyeball area that is extracted from the eye-region as the region of interes