Emotion recognition in dance therapy driven by DanceEmoNet: a deep learning model based on facial expression and pose estimation
Yadan Ye · 2026
Read Emotion recognition in dance therapy driven by DanceEmoNet: a deep learning model based on facial expression and pose estimation by Yadan Ye (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13193967.txt.
Description
Yadan Ye built a deep learning model that reads feelings in dance therapy in a 2026 paper. Dance therapy helps mental health, but feelings shift fast and are hard to track live. Past tools read just the face or just the body, and miss much of the change.
The model, DanceEmoNet, joins face reads with body pose reads in one net. It spots faces and poses, tracks them over time, and fuses the two streams. In tests it beat bench mark models on many scores, ran faster per frame, and cost less to run. Gains ran from five to ten percent, which suits live therapy use.
Catalog record
| CREATOR | Yadan Ye |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |