The dynamics of electric powered wheelchair sideways tips and falls: experimental and computational analysis of impact forces and injury
Bibliographic record
Abstract
BACKGROUND: To reduce the occurrence of wheelchair falls and to develop effective protection systems, we aimed to quantify sideways tip and fall dynamics of electric power wheelchairs (EPWs). We hypothesized that driving speed, curb height and angle of approach would affect impact forces and head injury risk for wheelchair riders. We further expected that fall dynamics and head injury risk would be greater for unrestrained riders compared to restrained riders. METHODS: Sideways wheelchair tip and fall dynamics were reconstructed using a remotely operated rear wheel drive EPW and a Hybrid III test dummy driving at different approach angles (5 to 63°) over an adjustable height curb (0.30 to 0.41 m) at speeds of 0.6-1.5 m/s. Rigid body dynamics models (Madymo, TASS International, Livonia, MI) were developed in parallel with the experiments to systematically study and quantify the impact forces and the sideways tip or fall of an EPW user in different driving conditions. RESULTS: Shallower approach angles (25°) (p < 0.05) and higher curbs (0.4 m) (p < 0.05) were the most significant predictors of tipping for restrained passengers. Unrestrained passengers were most affected by higher curbs (0.4 m) (p < 0.005) and fell forward from the upright wheelchair when the approach angle was 60°. Head impact forces were greater in unrestrained users (6181 ± 2372 N) than restrained users (1336 ± 827 N) (p = 0.00053). Unrestrained users had significantly greater head impact severities than restrained users (HIC = 610 ± 634 vs HIC = 29 ± 38, p = 0.00013) and several tip events resulted in HICs > 1000 (severe head injury) in unrestrained users. CONCLUSIONS: Sideways tips and forward falls from wheelchairs were most sensitive to curb height and approach angle but were not affected by driving speed. Sideways tips and falls resulted in impact forces that could result in concussions or traumatic brain injury and require injury prevention strategies. Seat belts eliminated the risk of falling from an upright chair and reduced head impact forces in sideways wheelchair tips in this study; however, their use must be considered within the ethical and legal definitions of restraints.
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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 | 0.000 |
| 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) | 0.001 | 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".