Radar-Based Hand Gesture Recognition With Feature Fusion Using Robust CNN-LSTM and Attention Architecture
In Human-Computer Interaction (HCI), seamless hand gesture recognition is essential for intuitive and natural interactions. Gestures act as a universal language, bridging the gap between humans and machines. Radar-based recognition surpasses traditional optical methods, offering robust interaction c...
Saved in:
| Main Authors: | Irshad Khan, Young-Woo Kwon |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10950371/ |
| 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) -
Microcontroller Implementation of LSTM Neural Networks for Dynamic Hand Gesture Recognition
by: Kevin Di Leo, et al.
Published: (2025-06-01) -
Hand Washing Gesture Recognition Using Synthetic Dataset
by: Rüstem Özakar, et al.
Published: (2025-06-01) -
Hand Gesture-Based Human-Computer Interaction using MediaPipe and OpenCV
by: Risma Dwi Tjutarjo Putri, et al.
Published: (2025-07-01) -
LSTM-Based Hand Gesture Recognition for Indonesian Sign Language System (SIBI) on Affix, Alphabet, Number, and Word
by: Patricia Ho, et al.
Published: (2025-06-01)