Validation of an innovative two-part algorithm for detecting self-propulsion in manual wheelchair users
Bibliographic record
Abstract
Abstract Introduction Actimetry is increasingly used to measure physical activity (PA) for MWC users. However, conversion of raw data into interpretable PA outcomes remains imprecise, and the differentiation between propulsion and non-propulsion is challenging. Using a previously developed two-part algorithm, the objectives of this study were to 1) measure the accuracy of total distance collected, and 2) validate the algorithm’s accuracy in differentiating between self-propulsion and non-propulsion. Methods Experimental study consisting of two data collection sessions. Actimetry data (Actigraph) were collected indoors (controlled conditions) during 100 repetitions (n=40 MWC propulsion, n=60 pushing the MWC) over three distances (10, 50 and 100 meters). Actimetry data (Actigraph) were also collected outdoors (uncontrolled condition) during self-propulsion over 1,000 meters (10 repetitions). Descriptive statistics (e.g., mean, standard deviation) with confidence intervals and accuracy measures (percentage of true value) were conducted for each trial. Results The algorithm measured total distance covered indoors with an excellent accuracy (98.9% to 99.8%). It differentiated between self-propulsion and non-propulsion with an accuracy between 96.2% and 99.2% under controlled condition, and between 91.3% and 100.0% under uncontrolled condition. Conclusions The algorithm tested allowed precise measurement of total distance covered, as well as an excellent discrimination between self-propulsion and non-propulsion.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".