Comparison of Strain-Gage and Fiber-Optic Goniometry for Measuring Knee Kinematics During Activities of Daily Living and Exercise
Bibliographic record
Abstract
There is increasing interest in wearable sensor technology as a tool for rehabilitation applications in community or home environments. Recent studies have focused on evaluating inertial based sensing (accelerometers, gyroscopes, etc.) that provide only indirect measures of joint motion. Measurement of joint kinematics using flexible goniometry is more direct, and still popular in laboratory environments, but has received little attention as a potential tool for wearable systems. The aim of this study was to compare two goniometric devices: a traditional strain-gauge flexible goniometer, and a fiberoptic flexible goniometer, for measuring dynamic knee flexion/extension angles during activity of daily living: chair rise, and gait; and exercise: deep knee bends, against joint angles computed from a "gold standard" Vicon motion tracking system. Six young adults were recruited to perform the above activities in the lab while wearing a goniometer on each knee, and reflective markers for motion tracking. Kinematic data were collected simultaneously from the goniometers (one on each leg) and the motion tracking system (both legs). The results indicate that both goniometers were within 2-5 degrees of the Vicon angles for gait and chair rise. For some deep knee bend trials, disagreement with Vicon angles exceeded ten degrees for both devices. We conclude that both goniometers can record ADL knee movement faithfully and accurately, but should be carefully considered when high (>120 deg) knee flexion angles are required.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".