Real-time performance of hand motion recognition using kinematic signals for impaired hand function training

2013 ◽  
Author(s):  
Dongrui Zhang ◽  
Yanjuan Geng ◽  
Xiufeng Zhang ◽  
Yuanting Zhang ◽  
Guanglin Li
Keyword(s):  
Real Time ◽  
Hand Function ◽  
Motion Recognition ◽  
Hand Motion ◽  
Time Performance

scholarly journals Motion Plane Estimation for Real-Time Hand Motion Recognition

2009 ◽  
Vol 16B (5) ◽  
pp. 347-358
Author(s):  
Seung-Dae Jeong ◽  
Kyung-Ho Jang ◽  
Soon-Ki Jung
Keyword(s):  
Real Time ◽  
Motion Recognition ◽  
Hand Motion ◽  
Motion Plane

Real-Time Hand Motion Recognition Using sEMG Patterns Classification

2018 ◽  
Author(s):  
Roxane Crepin ◽  
Cheikh Latyr Fall ◽  
Quentin Mascret ◽  
Clement Gosselin ◽  
Alexandre Campeau-Lecours ◽  
...  
Keyword(s):  
Real Time ◽  
Motion Recognition ◽  
Hand Motion

Real-time Performance Monitoring of MPC Based on Covariance Index Prediction

2014 ◽  
Vol 39 (5) ◽  
pp. 658-663 ◽  
Author(s):  
Xue-Min TIAN ◽  
Ya-Jie SHI ◽  
Yu-Ping CAO
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Time Performance

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

Sign in / Sign up

Export Citation Format

Share Document