Determining Fall Direction and Impact Location for Various Disturbances and Gait Speeds Using the Articulated Total Body Model
Bibliographic record
Abstract
Because fall experiments with volunteers can be both challenging and risky, especially with older volunteers, we wished to develop computer simulations of falls to provide a theoretical framework for understanding and extending experimental results. To perform a preliminary validation of the articulated total body (ATB) model for passive falls, we compared the model predictions of fall direction, impact location, and impact velocity as a function of disturbance type (faint, slip, step down, trip) and gait speed (fast, normal, slow) to experimental results with young adult volunteers. The three-dimensional ATB model had 17 segments and 16 joints. Its physical characteristics, environment definitions, contact functions, and initial conditions were representative of our experiment. For each combination of disturbance and gait speed, the ATB model was left to fall passively under gravity once disturbed, i.e., no joint torques were applied, until impact with the floor occurred. Finally, we also determined the sensitivity of the model predictions to changes in the model's parameters. Our model predictions of fall angles and impact angles were qualitatively in agreement with those observed experimentally for ten and seven of the 12 original simulations, respectively. Quantitatively, the model predictions of fall angles, impact angles, and impact velocities were within one experimental standard deviation for seven, three, and nine of the 12 original simulations, respectively, and within two experimental standard deviations for ten, nine, and 11 of the 12 original simulations, respectively. Finally, the fall angle and impact angle region did not change for 92% and 95% of the 74 input variation simulations, respectively, and the impact velocities were within the experimental standard deviations for 78% of the 74 input variation simulations. Based on our simulations and a sensitivity analysis, we conclude that our preliminary validation of the ATB model for passive falls was successful. In fact, these ATB model simulations represent a significant step forward in fall simulations. We believe that with additional work, the ATB model could be used to accurately simulate a variety of human falls and may be useful in further understanding the etiology and mechanisms of fall injuries such as hip fractures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".