Chieh-Ann Sun

Papers

Total Citations

H-Index

About

Chieh-Ann Sun is a researcher specializing in human activity recognition and human-robot interaction, with a particular focus on assistive technologies for smart home environments. Their most-cited work, "VQ-HMM classifier for human activity recognition based on R-GBD sensor" (2017, 8 citations), introduces a novel framework that leverages 3-D skeleton joint data captured by a Kinect sensor to interpret human activities. This system was specifically designed to serve as the visual component of a home robot, enabling more intuitive and responsive human-robot collaboration. By combining vector quantization (VQ) with hidden Markov models (HMM), Sun's approach effectively classifies complex human movements, enhancing the robot's ability to understand and anticipate user needs. This contribution is particularly significant for advancing ambient assisted living technologies, where seamless human-robot interaction is critical. Sun's work bridges the gap between computer vision and robotics, offering practical solutions for creating more humane and adaptable home robots. Their research continues to influence the development of intelligent systems that can autonomously support daily living activities.