[Research on first aid measures based on convolutional neural network recognition human actions].
Bibliographic record
Abstract
OBJECTIVE: To explore the application of human behavior recognition based on convolutional neural network (CNN) in the new generation of pre-hospital first aid. METHODS: Sixty videos were obtained from the Montreal Falling Video Data base, and divided into model training data and evaluation test data at a ratio of 5:1. (1) Data model training: singular value decomposition was used to clarify the picture, the target boundary of the human body in the picture was identified through target detection and Fourier transform, then the human body curve was described; OpenCv computer vision and machine learning software library to estimate the body pose were used to mark the important parts of the human body (such as hips, knees), the angle between the line of important parts and the horizontal direction and the length and width ratio of the detection frame were calculated, and whether the human body had abnormal behavior was identified. (2) Evaluation test: 6 videos were randomly extracted from the model training data set, 10 frame were extracted from each video, each frame was treated as one picture, CNN behavior recognition was used on each frame, and calculated the recognition rate between normal behavior and abnormal behavior. RESULTS: In the process of data model training, each frame was artificially labeled to train the CNN human behavior recognition model. The evaluation results showed that the recognition rate of normal behavior was (90.33±3.03)%, and the recognition rate of abnormal behavior was (87.74±2.88)%. CONCLUSIONS: When passers-by have dangerous behaviors, the identification of human behaviors through CNN can determine whether they are in a critical state, and issue early warning in time, which plays a vital role in pre-hospital first aid.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".