Papers
4
Total Citations
42
H-Index
2
About
Hanze Wang is a leading researcher in the field of human-robot interaction and rehabilitation engineering, with a primary focus on surface electromyography (sEMG)-based control for upper limb exoskeletons. His work addresses two critical challenges in neurorehabilitation: achieving subject-independent continuous joint angle estimation and developing practical home-based rehabilitation systems. Wang’s most cited paper (2022, 33 citations) introduces a novel approach combining multisource domain adaptation with a backpropagation neural network (BPNN) to enable continuous elbow angle estimation from sEMG signals, significantly reducing inter-subject variability—a longstanding barrier to clinical adoption. He further explored this variability in a 2022 study (5 citations), quantifying muscle activity differences across individuals. Notably, Wang has pioneered the translation of these algorithms into deployable systems, including a sEMG-controlled portable exoskeleton for home rehabilitation (2024) and a cloud-based telerehabilitation platform with bilateral control (2025). His work bridges the gap between laboratory-based myoelectric control and accessible, therapist-in-the-loop home therapy, directly addressing the needs of aging populations and hemiplegic patients. With cumulative citations approaching 50 and a clear trajectory toward clinical implementation, Wang is establishing himself as a key innovator in wearable robotic rehabilitation.
Research Focus
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Top Papers
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