A comparison of minimum segment models for the estimation of centre of mass position and velocity for slip recovery during a bathtub transfer task
Bibliographic record
Abstract
BACKGROUND: Exploring the use of minimum marker sets is important for balancing the technical quality of motion capture with challenging data collection environments and protocols. While minimum marker sets have been demonstrated to be appropriate for evaluation of some motion patterns, there is limited evidence to support model choices for abrupt, asymmetrical, non-cyclic motion such as balance disturbance during a bathtub exit task. RESEARCH QUESTION: How effective are six models of reduced complexity for the estimation of centre of mass (COM) displacement and velocity, relative to a full-body model. METHODS: Eight participants completed a bathtub exit task. Participants received a balance perturbation as they crossed the bathtub rim, stepping from a soapy wet bathtub to a dry floor. Six reduced models were developed from the full, 72-marker, 12 segment 3D kinematic data set. Peak displacement and velocity of the body COM, and RMSE (relative to the full-body model) for displacement and velocity of the body COM were determined for each model. RESULTS: Main effects were observed for peak right, left, anterior, posterior, upwards and downwards motion, and peak left, anterior, posterior, upwards and downwards velocity. Time-varying (RMSE) was smaller for models including the thighs than models not containing the thighs. In contrast, inclusion of upper arm, forearm, and hand segments did not improve model performance. The model containing the sacrum marker only consistently performed the worst across peak and RMSE metrics. SIGNIFICANCE: Findings suggest a simplified centre of mass model may adequately capture abrupt, asymmetrical, non-cyclic tasks, such as balance disturbance recovery during obstacle crossing. A reduced kinematic model should include the thighs, trunk and pelvis segments, although models that are more complex are recommended, depending on the metrics of interest.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".