Yangan Li
Papers
1
Total Citations
22
H-Index
1
About
Yangan Li is a rising researcher in biomedical engineering and human motion analysis, with a focus on lower limb activity recognition using surface electromyography (sEMG) signals. Their most-cited work, "An end-to-end lower limb activity recognition framework based on sEMG data augmentation and enhanced CapsNet" (2023), introduces a novel deep learning architecture that integrates data augmentation techniques with an enhanced Capsule Network (CapsNet) to improve the accuracy and robustness of activity classification from sEMG data. This framework addresses key challenges in wearable sensor-based motion analysis, such as limited training data and signal variability, achieving superior performance over traditional methods. With 22 citations in a short time, the paper has already influenced research in prosthetics control, rehabilitation monitoring, and human–machine interfaces. Li’s contributions are particularly notable for advancing end-to-end learning pipelines that reduce preprocessing burdens while enhancing generalization. Their work holds promise for real-time, non-invasive applications in assistive technologies and clinical diagnostics, marking them as an emerging voice in the intersection of deep learning and biomedical signal processing.
Research Focus
Key Achievements
Top Papers
- 1