The assessment of stationary and locomotion physical behavior using a single versus dual wearable accelerometer in children who use a manual wheelchair
Bibliographic record
Abstract
AIMS: Wearable devices such as activity monitors can be used to gain insight into children's physical behavior. However, children who are unable to walk are often excluded from validation studies, and therefore, robust information about their physical behavior is lacking. Therefore, we studied the criterion validity of a wearable prototype activity monitor (AM-p) in children who use a manual wheelchair with and without the ability to walk. METHODS: We analyzed the data for both single-sensor (sensor placement on upper arm or ankle) and dual-sensor use. Therefore, we conducted a study with cross-sectional design, assessing 37 children (12 girls) aged 6-19 years (mean 12 years, SD 4.3). Children wore an AM-p on the ankle and upper arm and were filmed while performing an activity protocol in a natural setting. Videos were labeled per 5-second epoch with individual activity labels. Raw data were synchronized with labels. An algorithm was trained, and labels were subdivided into pre-defined activity categories. Overall accuracy and F1 score (harmonic mean of precision and recall) were calculated per activity. FINDINGS: We demonstrate that the single ankle-worn AM-p can determine "stationary" behavior with excellent accuracy (>90%) and "locomotion" behavior with moderate to good accuracy (77-80%). "Locomotion" behavior includes active wheelchair use of children, which can assist pediatric physical therapists (PPTs) to assess physical behavior correctly in children who use a manual wheelchair. Exploratory analyses indicate that "locomotion" behavior (dual-sensor use), can be divided into "leg activity" and "active wheelchair use" for children who use a manual wheelchair and have the ability to walk. CONCLUSIONS: The single ankle-worn AM-p can determine "stationary" behavior with excellent accuracy and "locomotion" behavior with moderate to good accuracy in children who use a manual wheelchair in daily living. Our findings can assist PPTs to assess physical behavior correctly and tailor individual treatment plans.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".