Ambulatory Center of Mass Prediction Using Body Accelerations and Center of Foot Pressure
Bibliographic record
Abstract
The center of body mass (COM), center of foot pressure (COP), and body segment acceleration signals are commonly used to indicate movement performance and stability during standing activities and walking. For balance maintenance and restoration, the human brain is capable of estimating and predicting the COM even in the absence of visual or vestibular information. Thus, we hypothesized that the COM may be acquired through the processing of proprioceptive somatosensory information, represented by body segment accelerations, and an external spatial reference, the ground support, represented by the COP. To investigate this hypothesis, we modeled the relationships that exist between the COP and accelerometer data with the 3-D COM trajectory, during walking on firm and irregular surfaces. The models accounted for 99.85 +/- 0.20% and 99.77 +/- 0.39% of the resultant COM trajectory's variability for the firm and irregular surfaces, respectively. This corresponded to a percentage error between the estimated and actual resultant COM of 16.06 +/- 11.11% for the firm surface and 21.41 +/- 12.70% for the doweling surface. In turn, this translates into an absolute error between the true and actual resultant COM of 3.62 +/- 2.69 cm and 4.74 +/- 3.01 cm for the firm and doweling surfaces, respectively. The model is novel in that it does not require any calibration and provides a reasonably accurate estimation of the COM, which can be compared to the brain's balance performance. Hence, this model could be used instead of the cumbersome method of video motion analysis for COM calculation.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".