Suxia Xu

Papers

Total Citations

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About

Suxia Xu is a leading researcher in human activity recognition and underwater robotics perception, whose work bridges the gap between machine learning and real-world autonomous systems. Her foundational research on human activity recognition, published in 2014, introduced a novel hybrid approach combining Support Vector Machines (SVM) with Hidden Markov Models (HMM) to accurately classify daily living activities using RGBD sensors. This work, which has garnered 50 citations, remains a key reference for applications in personal assistive robotics and smart home environments. In parallel, Xu has made significant contributions to underwater robotics, developing an advanced image enhancement algorithm that dramatically improves visual clarity in turbid water conditions. Her 2018 paper on this topic, cited 30 times, directly addresses critical challenges in underwater target detection, robot navigation, and obstacle avoidance. By enabling clearer environmental recognition, Xu’s work has practical implications for marine science and autonomous underwater vehicles. Her research exemplifies how robust sensor data processing can unlock new capabilities for robots operating in complex, unstructured environments—from homes to the deep sea.