Xuena Chen
Papers
1
Total Citations
35
H-Index
1
About
Xuena Chen is a rising researcher at the intersection of neuromorphic computing and human–machine interaction, with a primary focus on sign language recognition and gesture-based communication systems. Her most cited work, "Sign Language Gesture Recognition and Classification Based on Event Camera with Spiking Neural Networks" (2023, 35 citations), introduces a groundbreaking approach that leverages event cameras—sensors with high temporal resolution and low energy consumption—paired with spiking neural networks to overcome the limitations of traditional frame-based vision systems. This contribution addresses a critical need for efficient, real-time recognition of sign language, directly improving accessibility for individuals with speech impairments. By reducing visual redundancy and computational load, Chen’s research advances the practicality of nonverbal communication interfaces in human–machine interactions. Her work exemplifies the synergy between biologically inspired computing and socially impactful technology, offering a pathway toward more inclusive and energy-efficient assistive devices. As an emerging voice in neuromorphic engineering, Chen’s innovations hold promise for transforming how machines understand and respond to human gestures in real-world settings.
Research Focus
Key Achievements
Top Papers
- 1