Student Competition (Technology Innovation) ID 1986855
Bibliographic record
Abstract
Background Up to 70% of individuals with spinal cord injury (SCI) experience shoulder injuries during their lifetime. Previous studies revealed a link between the risk of shoulder injury and propulsion-related kinetic and kinematic parameters that were measured using SMARTWheel or in-lab motion-capture systems. Despite their high accuracy, these systems are time and labour intensive and not commonly accessible. Objective To develop and validate a portable and accessible method to estimate the duration of the push phase using a hand-mounted inertial measurement unit (IMU). Methods Ten volunteers (7 males, 3 females, age: 28 ± 2 y.o.) consented to participate in the study. An IMU (3D acceleration and angular velocity, sampling frequency: 512 Hz) was attached to participant’s right hand while sitting on the instrumented wheelchair equipped with SMARTWheel (sampling frequency: 240 Hz). The SMARTWheel and IMU readouts were collected while participants were propelling the wheelchair. The peaks in the resultant acceleration and continuous wavelet transform coefficients obtained from IMU were used to identify the hand contact and release, and estimate the push phase duration. Results No significant differences (p-value = 0.97, 0.89, and 0.94, respectively) were observed between the parameters obtained for the hand contact and release instants and push duration estimated using IMU compared to SMARTWheel with mean errors (standard deviation) of 8.4 (15.2) ms, 3.8 (22.1) ms and −4.6 (24.6) ms, respectively. Conclusion These findings support the validity of using IMU as a portable alternative to the in-lab systems to estimate the push phase duration of manual wheelchair users.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.900 | 0.816 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".