Cheng-Jung Lee

Chang Gung Memorial Hospital, National Yunlin University of Science and Technology

Papers

Total Citations

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About

Cheng-Jung Lee is a researcher specializing in assistive robotics and intelligent diagnostic systems, with a focus on enhancing human-robot interaction and operational reliability. His key research areas include adaptive guidance systems for assistive technologies and machine learning-driven fault diagnostics. Lee’s most notable contribution is the design of an adaptive guidance system for an assistive robotic walker (2015), which has garnered 30 citations, underscoring its impact on mobility assistance for elderly and disabled users. This work integrates real-time sensor feedback and user intent recognition to improve safety and autonomy. Additionally, his 2019 study introduces a machine learning approach for robot diagnostic systems, employing acoustic filtering techniques on an industrial embedded Compact-RIO platform to enable efficient fault detection. While this paper has 2 citations, it represents a forward-looking application of ML in predictive maintenance. Lee’s research bridges practical robotics and AI, offering solutions that enhance both user experience and system robustness. His work is particularly valuable for students and researchers exploring adaptive control, assistive devices, and intelligent diagnostics in robotics.