Neural network and system for attitude and behavior detection based on pressure data
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
In the process of monitoring the behavior of the elderly, wearable devices and visual devices are easily limited by the site and environment, resulting in poor monitoring results. This paper proposes a posture behavior detection method and system based on pressure data. The convolutional neural network algorithm is used to identify the pressure data to detect the posture, calculate the posture holding time and posture change frequency, judge the posture change action process according to the trajectory of the pressure center point, and finally record and analyze the user's behavior. The correct rate of pose classification of the model used in this paper has reached 98.69%, and the correct rate of pose retention time has reached 98.06%. Finally completed the research and development of the relevant monitoring system, which can be used in the field of medical treatment and daily care.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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