Yu-Jui Chang
Papers
2
Total Citations
25
H-Index
2
About
Yu-Jui Chang is a researcher at the forefront of human–robot interaction and assistive technology, with a particular focus on sensor-based gesture recognition and rehabilitation robotics. His work centers on leveraging low-cost, accessible hardware—most notably the Microsoft Kinect sensor—to create intelligent systems that bridge the gap between human motion and machine understanding. In his most cited study, Chang developed a Hidden Markov Model (HMM) framework with improved feature extraction for identity recognition of gesture command operators, achieving 19 citations for its novel approach to processing Kinect-data streams. His equally impactful 2016 work introduced a sport instructor robot designed specifically for the rehabilitation and exercise training of elderly individuals. By employing the Kinect sensor as the “eyes” of a humanoid robot, Chang’s system could capture, recognize, and respond to a person’s gestures in real time, enabling autonomous, personalized coaching. This contribution is notable not only for its technical innovation in sensor fusion and pattern recognition but also for its direct application to geriatric care and physical therapy. Through these efforts, Chang has demonstrated how affordable sensing technology can be repurposed to address critical challenges in aging populations and human–robot collaboration.
Research Focus
Key Achievements
Top Papers
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