Papers
2
Total Citations
139
H-Index
2
About
Sangwoo Mo is a leading researcher in the field of tactile sensing for robotics, with a primary focus on electrical impedance tomography (EIT)-based sensor systems. His work addresses the critical challenge of developing large-area, durable, and scalable tactile sensors for safe human-robot interaction. Mo’s major contribution lies in pioneering deep neural network (DNN) approaches to overcome the traditional limitation of poor spatial resolution in EIT-based tactile sensors. His most-cited paper, "Deep Neural Network Based Electrical Impedance Tomographic Sensing Methodology for Large-Area Robotic Tactile Sensing" (2021, 109 citations), demonstrates how DNNs can significantly enhance reconstruction accuracy while maintaining the practical benefits of sparse electrode configurations—such as durability and low fabrication cost. His earlier work, "Deep Neural Network Approach in Electrical Impedance Tomography-based Real-time Soft Tactile Sensor" (2019, 30 citations), further established the feasibility of real-time, whole-body tactile sensing. By merging machine learning with soft sensor technology, Mo has enabled robots to interact more safely and intuitively with their environments, making his research foundational for the next generation of human-robot collaboration systems.
Research Focus
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