Measurement of shank angle during stance using laser range finders
Bibliographic record
Abstract
Ankle joint stiffness, the dynamic relationship between the joint angle and the torque acting about it, plays an important role in the control of upright stance. In order to identify the contribution of ankle joint stiffness to stance control, ankle joint must be perturbed externally. One way to do this is to displace the foot that will cause shank movement. For identification, the ankle angle must be measured with high accuracy, for which we need to measure both foot and shank angles. However, most motion capture systems do not have the resolution and accuracy needed to measure the small ankle joint movements that occur during stance. This paper describes a method for the high resolution measurement of ankle angle during standing that uses a laser range finder to track linear displacements, which is then used to compute shank angle with respect to the vertical. A theoretical analysis of different possible measurement configurations demonstrated that measurements of horizontal shank movement would provide the optimal resolution; a range finder with a linear resolution of 25 micros would provide an angle resolution better than 0.01 degree. We built a measurement system using this configuration and performed static and dynamic experiments that demonstrated angle measurements with a resolution of less than 0.01 degree, which outperforms other motion capture systems, such as IMUs, whose resolution is in the order of one degree. Utility of the method was then demonstrated by using it to measure shank ankle during quiet and perturbed stance. The results confirmed that the method tracks small shank movements during both quiet and perturbed conditions. Estimated shank angle then was used with the foot angle, measured with a potentiometer to obtain the ankle joint angle, needed to identify the joint stiffness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".