Massage Acupoint Positioning Method of Human Body Images Based on Transfer Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Traditional Chinese massage therapy is a very popular method to stay healthy, which regulates body balance, alleviates fatigue, and prevents diseases by massaging specific acupoints.Although computer vision has been increasingly applied in traditional Chinese medicine, related study of acupoint positioning is still insufficient.The existing acupoint positioning methods mainly rely on manual labeling and rule matching, which often require a large amount of manual intervention with limited accuracy.Therefore, this study proposed a massage acupoint positioning method of human body images based on transfer learning.The massage acupoint meridian and collateral positioning principle of human body images was presented.Using the integrated deep belief network model as a pre-trained model, a feasible transfer learning model was established through fine-tuning and feature mapping.The experimental results verified that the proposed method was effective.Relevant research results provide useful references for research in related fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it