The 11th session of the CSTH Spring Seminar Series 2022 is presented by graduate student in computer science and statistics Nick Littlefield.
Autonomous detection, localization, and classification of hand gesture has been a very active research area in computer vision. Many different AI-powered models of hand gesture recognition are now available which, aside from lending theoretical contributions to other fields, have already demonstrated successful applications in a variety of domains, including but not limited to human robot interaction, sport, virtual reality, assistive devices, and healthcare. In this talk, we together review, from a computational perspective, the basic concepts of AI-powered hand gesture recognition and its potential applications in healthcare. We will start from fully-annotated datasets to deep learning detection and classification. We will explore what autonomous hand gesture recognition algorithms are, what they do, and how.
Nick Littlefield is a graduate student in Computer Science and Statistics. He is currently a graduate research assistant for the HexAI research lab at USM working with a multidisciplinary team of faculty members, students, and investigators to design, build, and implement artificial intelligence (AI)-Enabled data analytics in different healthcare settings, such as public health and orthopedics. His primary interests lie in software development and artificial intelligence in the healthcare industry.