Hongmin Wang

Papers

Total Citations

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About

Hongmin Wang is a researcher whose work bridges intelligent robotic systems and human-assistive technologies, with a particular focus on data management for robotics and wearable exoskeleton control. His early contributions addressed the growing challenge of handling large-scale heterogeneous sensor data in robotic systems, proposing a partitioning and indexing algorithm for Resource Description Framework (RDF) data in cloud-based environments — a foundational work that has garnered 4 citations and speaks to the increasing complexity of multi-sensor robotic architectures. More recently, Wang has turned his attention to lower-limb exoskeleton robotics, specifically the critical problem of gait phase recognition. His work applying CNN and HHO-SVM models, as well as CNN-LSTM architectures, to hip exoskeleton systems demonstrates a commitment to improving the compliance control and real-world usability of wearable assistive devices. These studies leverage inertial measurement units and advanced deep learning techniques to achieve accurate, real-time motion phase detection — an essential prerequisite for safe exoskeleton operation. Though still accumulating citations, Wang's research trajectory reflects a thoughtful evolution from robotic data infrastructure toward human-centered robotics, positioning him as an emerging contributor to the field of intelligent assistive technologies.