A portable inertial sensing-based spinal motion measurement system for low back pain assessment
Bibliographic record
Abstract
Spinal motion measurement during dynamic conditions may help identify differences between individuals with and without low back pain (LBP). The purpose of this paper is to demonstrate the feasibility of an inertial sensing based, portable spinal motion measurement system for investigating the differences of the spinal motions between an LBP group and a healthy control group. During a fast flexion/extension test, we measured 3D angular motions of the pelvis, lumbar spine and thoracic spine of the two groups using the inertial sensing based system. Range of motions (ROM) and peak angular velocities were investigated to determine which variables have significant differences between the two groups (p < 0.05). Also, a logistic regression analysis was carried out to see the classifying ability of the LBP patients from controls using the proposed system. The result shows that LBP was particularly associated with significant decreases in peak velocities of the lumbar spinal extension motion, having the maximum 90% sensitivity and 80% specificity in the classification according to the regression analysis. The result demonstrates the possibility of the proposed inertial sensing-based system to be served as an efficient tool in providing an accurate and continuous measurement of the spinal kinematics.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".