Research on posture filtering algorithms for lower limb rehabilitation of patients with functional impairment in sports
Bibliographic record
Abstract
This paper carries out a research on patients' lower limb posture capture strategy based on the lower limb rehabilitation of patients with sports function injury.The study is based on the posture filtering algorithm and designed a lower limb joint localization model based on the quaternion Kalman filter.The model utilizes five IMUs to capture the patient's lower limb movements to determine the posture of the patient's critical limbs in three-dimensional space and establish the joint coordinate system.Based on the filtered pose quaternions, the joint coordinate system of the lower limb is solved to obtain the optimal estimation of the lower limb pose.The results of simulation experiments show that the algorithm of this paper can make the motion data smoother and satisfy the motion requirements.The valuation of this paper's algorithm on the Z-axis in the single-axis rotation experiment is stable from -90° to 90°, while the valuation on the X-axis and Y-axis is near 0°.And the error in the ankle motion trajectory is small, with a mean value of 1.36°.The example results illustrate that the rehabilitation system equipped with the algorithm of this paper is basically consistent with the thigh elevation curve of the optical method in the patient's lower limb motion monitoring during walking, and the error is within 6°.The research in this paper provides a new technical means for lower limb rehabilitation training, which helps to improve the personalization and precision of rehabilitation training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".