Challenges and Applications for Hand Gesture Recognition

  • 4h 57m
  • Bhupesh Kumar Dewangan
  • IGI Global
  • 2022

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.

About the Author

Bhupesh Kumar Dewangan pursued Ph.D. in computer science and engineering from University of Petroleum and Energy Studies, Dehradun, India, and Master of Technology from Chhattisgarh Swami Vivekananda Technical University (State Technical University), Bhilai, India in computer science and engineering and Bachelor of Technology from Pandit Ravi Shankar Shukla University (State University), Raipur, India. He is currently working as an Associate Professor in the Department of Computer Science and Engineering, School of Engineering at the OP Jindal University Raigarh, India. He has more than 50 research publications in various international journals and conferences with SCI/SCOPUS/UGC indexing. He has three Indian patents on Cloud computing and resource scheduling. His research interests are in Autonomic Cloud Computing, Resource Scheduling, Software Engineering, and Testing, Image processing, and Object detection. He is a member of various organizations like ISTE, IAPFE, etc. Currently, he is editor in special issue journals of Inderscience & IGI publication house, and editor/author of two books of Springer & Taylor and Francis publication house.

In this Book

  • Introduction
  • Skin-Colour-Based Hand Segmentation Techniques
  • Hand Gesture Recognition Through Depth Sensors
  • Hand Gesture Recognition—The Road Ahead
  • IoT-Based Wearable Sensors—Hand Gesture Recognition
  • Recent Advancements in Design and Implementation of Automated Sign Language Recognition Systems
  • Hidden Markov Model for Gesture Recognition
  • A Six-Stream CNN Fusion-Based Human Activity Recognition on RGBD Data—A Novel Framework
  • Multi-Input CNN-LSTM for End-to-End Indian Sign Language Recognition—A Use Case with Wearable Sensors
  • Lightweight ConvNet Model for American Sign Language Hand Gesture Recognition
  • Applications of Hand Gesture Recognition
  • Application of HGR Rock-Paper-Scissors Model
  • Compilation of References