Weiliang Xie
Papers
1
Total Citations
7
H-Index
1
About
Weiliang Xie is a researcher advancing the field of human action recognition, with a focus on wearable sensor-based systems for applications in human–robot interaction, healthcare, and sports analytics. His most-cited work, "Human Action Recognition Based on Hierarchical Multi-Scale Adaptive Conv-Long Short-Term Memory Network" (2023, 7 citations), introduces a novel deep learning architecture that integrates convolutional and LSTM networks to capture spatio-temporal motion patterns from sensor data. This hierarchical, multi-scale adaptive approach addresses the challenge of extracting meaningful movement features from noisy, real-world wearable device inputs, improving recognition accuracy and robustness. Xie’s contributions are particularly impactful in enabling more intuitive human–robot collaboration and remote health monitoring, where precise activity detection is critical. His work has garnered early attention in the community, with citations growing as wearable technology becomes more pervasive. By bridging signal processing and deep learning, Xie is helping to make human action recognition more practical and reliable, laying groundwork for smarter, context-aware systems that respond to human movement in real time.
Research Focus
Key Achievements
Top Papers
- 1