Papers
7
Total Citations
370
H-Index
7
About
Boxuan Zhong is a leading researcher at the intersection of computer vision, wearable robotics, and artificial intelligence, with a primary focus on enhancing the autonomy and safety of lower-limb prostheses and exoskeletons. His most impactful work addresses the critical challenge of environmental context prediction—enabling prosthetic limbs to anticipate and adapt to changing terrains in real time. His 2020 paper on vision-based context prediction for lower-limb prostheses (66 citations) introduced a novel framework that simultaneously predicts a user’s environmental context while quantifying prediction uncertainty, a key step toward reliable, terrain-adaptive control. Zhong also pioneered the use of convolutional neural networks for automated species-level identification of planktic foraminifera (2019, 96 citations), demonstrating remarkable accuracy that rivaled human experts. His contributions extend to robust grasping target recognition for upper-limb prostheses (2020, 53 citations) and the fusion of human gaze with machine vision to predict intended locomotion modes (2022, 29 citations). With over 370 total citations across his top papers, Zhong’s work is widely recognized for bridging the gap between computer vision algorithms and real-world wearable robotic systems, advancing both the reliability and efficiency of assistive technologies.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Dynamically Modulated Mask Sparse Tracking53 citations · 2016
- 4Reliable Vision-Based Grasping Target Recognition for Upper Limb Prostheses53 citations · 2020
- 5Efficient Environmental Context Prediction for Lower Limb Prostheses40 citations · 2021
- 6
- 7