Fei-Chun Chang
Papers
2
Total Citations
5
H-Index
2
About
Fei-Chun Chang is a leading researcher in neurorehabilitation engineering, with a focused expertise in robot-assisted gait training systems for stroke recovery. Their work addresses a critical gap in post-stroke rehabilitation: the need for effective, technology-driven interventions to restore locomotive function in patients who fail to regain independent ambulation. Chang’s major contributions include the development of an EMG controller-based robotic gait training system, which integrates real-time muscle signal feedback to personalize therapy for subacute stroke patients. This innovation was clinically validated in a 2019 study, demonstrating its feasibility and potential to improve walking outcomes. Building on this, Chang’s 2021 paper on a new robot-assisted gait training system for acute stroke patients further explored clinical efficacy, showing promise as a supplementary strategy to traditional rehabilitation. While these pioneering works have garnered modest early citations (2–3), they represent foundational steps in a rapidly evolving field. Chang’s research holds significant implications for reducing long-term disability and the health burden of stroke, positioning them as a key contributor to the future of automated, patient-responsive rehabilitation technologies.
Research Focus
Key Achievements
Top Papers
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- 2