Monitoring wheelchair propulsion patterns: feasibility and validity of using wearable sensors
Bibliographic record
Abstract
Currently, there is a need to understand the characteristics of manual wheelchair propulsion patterns in the daily life of users and the impact of these patterns on repetitive strain injury of the shoulders. This study aimed to develop a method for remote and long-term monitoring of wheelchair propulsion techniques. We used a hand-mounted inertial measurement unit (IMU) to identify propulsion patterns in manual wheelchair users. IMU data was collected from 12 participants (7 males and 5 females), including 8 experienced and 4 inexperienced manual wheelchair users. We applied continuous wavelet transform (CWT) for feature extraction and used Support Vector Machine (SVM) and Multilayer Perceptron (MLP) Neural Network for pattern classification. SVM with a linear kernel achieved 89% accuracy, 78% F1-score, 78% precision, and 78% recall. SVM with a polynomial kernel achieved 94% accuracy, 88% F1-score, 88% precision, and 89% recall, while the MLP reached 95% accuracy, 89% F1-score, 89% precision, and 89% recall. Neither the participants’ wheelchair experience nor their gender significantly affected the performance of the classifiers. These findings suggest that the proposed IMU and propulsion patterns classification method can be used across different user profiles for remote and long-term monitoring of wheelchair propulsion patterns to better understand shoulder overuse risk in daily life.
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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 | 0.003 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".