Guest Editors
Prof. Jungpil Shin
Email: jpshin@u-aizu.ac.jp
Affiliation: School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
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Research Interests:Pattern recognition, image processing, computer vision, machine learning, human-computer interaction, non-touch interfaces, human gesture recognition, automatic control, Parkinson’s disease diagnosis, ADHD diagnosis, user authentication, machine intelligence, bioinformatics, as well as handwriting analysis, recognition, and synthesis
Prof. Yong Seok Hwang
Email: thestone@kw.ac.kr
Affiliation: Department of Electronic Engineering, Kwangwoon University, Seoul 01897, South Korea
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Research Interests: Volumetric holographic optical element (VHOE)-based augmented reality (AR) devices, such as transparent projection display, HUD and holo glasses and integral imaging display, hologram image processing, machine learning, human–computer interaction, non-touch interfaces, human gesture recognition, ADHD and autism diagnosis, and digital therapeutics
Summary
The rapid advancement of artificial intelligence (AI), machine learning (ML), and sensor-based technologies has propelled the field of activity recognition to new insights. However, the complexity of real-world scenarios poses ongoing challenges that require innovative solutions. This Special Issue aims to bridge the gap between activity recognition and its practical applications across various domains like human–computer interaction (HCI), virtual reality, security, the Internet of Things (IoT), and healthcare facilities.
Over the past few decades, video-based and sensor-based human activity recognition (HAR) has become a prominent area of research, driven by advances in computational power, improved camera and sensor technologies, and sophisticated machine learning and deep learning algorithms. The goal of this Special Issue is to explore the next generation of activity recognition, focusing on the latest methods, unresolved challenges, and cutting-edge solutions.
· Highlight novel methodologies and approaches in activity recognition.
· Address current challenges and limitations in the field.
· Propose and discuss practical solutions that enhance the accuracy, efficiency, and applicability of activity recognition systems.
· Explore the integration of activity recognition with emerging technologies such as AI, IoT, and immersive environments.
· Evaluate the social, ethical, and practical implications of deploying next-generation activity recognition technologies.
Keywords
Sensor; Human Activity Recognition; Machine Learning; Artificial Intelligence
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