Hai Chang

Papers

Total Citations

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About

Hai Chang is a researcher whose work sits at the intersection of biomedical signal processing and human activity recognition, with a particular focus on surface electromyography (sEMG) for lower-limb applications. His most notable contribution is the creation of the "HAR-sEMG" dataset, a publicly available resource designed to advance the development of machine learning models for recognizing human activities from lower-limb sEMG signals. This dataset, published in 2021 and already accumulating 18 citations, addresses a critical gap in the field by providing standardized, high-quality data for training and benchmarking algorithms in prosthetics, rehabilitation, and wearable robotics. By enabling more accurate and robust activity recognition, Chang’s work has direct implications for improving assistive technologies and human-machine interfaces. His research is particularly valuable for students and engineers working on real-time control of lower-limb exoskeletons or prosthetic devices, where reliable sEMG-based classification is essential. With a growing citation impact, Hai Chang is establishing himself as a key contributor to the practical deployment of biosignal-driven systems in healthcare and robotics.