Papers
4
Total Citations
149
H-Index
3
About
Gang Tang is a multidisciplinary researcher whose work spans wearable technology, human-machine interfaces, neural network optimization, and minimally invasive surgical techniques. His most influential contribution, "Hybridized wearable patch as a multi-parameter and multi-functional human-machine interface" (2020, 98 citations), demonstrates his expertise in developing advanced sensing systems that bridge human physiology and machine control — a field with profound implications for rehabilitation engineering and smart health monitoring. Complementing this, his work on PSO-LSTM modeling for continuous upper limb joint angle estimation (37 citations) showcases his ability to harness deep learning and evolutionary optimization to decode surface electromyography signals, advancing the control of assistive exoskeletons for patients with motor impairments. Notably, Tang's research portfolio also extends into surgical innovation. His meta-analyses comparing robotic, laparoscopic, and open pancreaticoduodenectomy approaches (11 and 3 citations respectively) reflect a commitment to evidence-based surgical practice, helping clinicians navigate the evolving landscape of minimally invasive pancreatic surgery. Together, these contributions reveal a researcher of remarkable breadth — equally comfortable pushing boundaries in bioelectronics and informing life-saving surgical decisions through rigorous systematic analysis.
Research Focus
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Top Papers
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