Alignment-Enhanced Interactive Fusion Model for Complete and Incomplete Multimodal Hand Gesture Recognition
Hand gesture recognition (HGR) based on surface electromyogram (sEMG) and Accelerometer (ACC) signals is increasingly attractive where fusion strategies are crucial for performance and remain challenging. Currently, neural network-based fusion methods have gained superior performance. Nevertheless,...
Saved in:
| Main Authors: | Shengcai Duan, Le Wu, Aiping Liu, Xun Chen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10323506/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Survey on Hand Gesture Recognition From Visual Input
by: Manousos Linardakis, et al.
Published: (2025-01-01) -
Temporal relationships between speech and hand gestures in the vicinity of potential turn boundaries in German and Swedish conversation
by: Margaret Zellers, et al.
Published: (2025-01-01) -
Hand Washing Gesture Recognition Using Synthetic Dataset
by: Rüstem Özakar, et al.
Published: (2025-06-01) -
Fusion-Optimized Multimodal Entity Alignment with Textual Descriptions
by: Chenchen Wang, et al.
Published: (2025-06-01) -
RETHINKING (DIS)FLUENCY WITHIN THE SCOPE OF INTERACTIONAL LINGUISTICS AND GESTURE STUDIES
by: Loulou KOSMALA
Published: (2022-08-01)