.png){kind=icon}
.png){kind=icon}
Return
Artificial intelligence for automatic movement recognition: a network-based approach
.png){kind=icon}
OA
AI
DOI：10.1016/j.array.2026.100857.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Movement recognition assessment via network-based kinematic features (kinectomes). • Kinectomes classify 30 movements performed by 168 athletes with 99% accuracy. • Kinectomes outperform PCA in accuracy, robustness, and feature interpretability. • Kinectomes remain stable even with missing or derivative kinematic data.
Keywords:
Human movement recognition
Coordination
Network theory
Artificial intelligence
Movement classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.5
Papers:
776
Citations:
1.2K
Organization
View more organizations