Challenges And Applications For Hand Gesture Recognition

Due to the rise of new applications in electronic appliances and pervasive devices, automated hand gesture recognition (HGR) has become an area of increasing interest. HGR developments have come a long way from the traditional sign language recognition (SLR) systems to depth and wearable sensor-based electronic devices. Where the former are more laboratory-oriented frameworks, the latter are comparatively realistic and practical systems. Based on various gestural traits, such as hand postures, gesture recognition takes different forms. Consequently, different interpretations can be associated with gestures in various application contexts. A considerable amount of research is still needed to introduce more practical gesture recognition systems and associated algorithms. Challenges and Applications for Hand Gesture Recognition highlights the state-of-the-art practices of HGR research and discusses key areas such as challenges, opportunities, and future directions. Covering a range of topics such as wearable sensors and hand kinematics, this critical reference source is ideal for researchers, academicians, scholars, industry professionals, engineers, instructors, and students.

This book constitutes the refereed proceedings of the 6th International Conference on New Trends in Information and Communications Technology Applications, NTICT 2022, held in Baghdad, Iraq, during November 16–17, 2022. The 14 full papers included in this book were carefully reviewed and selected from 53 submissions. They were organized in topical sections as follows: computing methodologies; information systems; network; security and privacy.

This book gathers selected high-quality research papers from the International Conference on Computational Methods and Data Engineering (ICMDE 2020), held at SRM University, Sonipat, Delhi-NCR, India. Focusing on cutting-edge technologies and the most dynamic areas of computational intelligence and data engineering, the respective contributions address topics including collective intelligence, intelligent transportation systems, fuzzy systems, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing.

Even though automatic hand gesture recognition technology has been applied to real-world applications with relative success, there are still several problems which need to be addressed for wider applications of Human-Computer Interaction. One of such problems which arise in hand gesture recognition is to spot meaningful gestures from the continuous sequence of the hand motions. Another problem is caused by the fact that there is quite a bit of variability (i.e. in shape, trajectory and duration) in the same gesture even for the same person. Throughout literature, the backward spotting technique is used which first detects the end points of gestures and then tracks back through their optimal paths to discover the start points of gestures. Upon the detection of the start and the end points, in between points trajectory is sent to the recognizer for recognition. So, a time delay is observed between the meaningful gesture spotting and recognition. This time delay is unacceptable for online applications. Given the fact of high variability of corresponding gesture to other gestures, modeling the other gesture is a vital issue to accommodate the infinite number of non-gesture patterns.

"This book provides an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research"--Provided by publisher.

The approaches to computer vision have undergone a long journey in recent years, but still, innovations are continuing with leverage increases in computing power, new data availability, and new ways to leverage machine-learning algorithms. As a branch of artificial intelligence (AI), computer vision brings meaningful information from images and videos. Such innovations help communicators to run better campaigns, amplify messages further, and stand out in a noisy, crowded marketplace. Investigations in Pattern Recognition and Computer Vision for Industry 4.0 provides a holistic discussion of the new practical applications and use cases of computer vision and communications. Covering topics such as social media filters, mobile computer vision, and AI-powered image editing, this book is ideal for academicians, researchers, postgraduate students, professional data analysts, research and development centers, organizations dealing with healthcare informatics, and IT firms.