Adaptive Biking for Children with Cerebral Palsy
Bibliographic record
Abstract
Evidence to understand the effects of adaptive biking on physical performance has only recently started to surface. There is the emerging potential for the use of an adaptive bike as a therapeutic intervention to improve the physical function of children and adolescents with Cerebral Palsy. The purpose of this study was to assess the effectiveness of physical performance measures to integrate with an adaptive bike for capturing the physical performance of a rider; and to establish a baseline intervention study protocol for future use of measuring performance on an adaptive bike. A feasibility study focused on exploring the physical performance of power output using NCTE_128 BB torque sensor, range of motion using Delsys® Goniometer, Kinovea Beta, and Altius Analytics Labs, postural data using FSA pressure mapping system of two riders engaged in dynamic biking, and to detect potential technological issues to future study. NCTE_128BB torque sensor, although, feasible to integrate with an adaptive bike, captured data were not useful for this setting due to the sensor’s limitation to capture data at low values. Evaluation of knee ROM during dynamic biking proved to be demanding. Altius was identified as the best option, given the lack of needing to place sensors on the limbs, the automation, and the data accuracy. The FSA mat exhibited consistent performance and produced robust data in each trial. For future research, using the most updated version of these tools will provide a better opportunity for a clinician to capture and analyze data with relative ease. Overall, both the riders were able to perform all biking trials without any difficulty. Based on the results of this study, the baseline protocol for Phase II was outlined. The study guided the intent to select physical performance measures that can effectively quantify physical performance to evaluate potential change in performance over time. Study findings suggest conducting a feasibility study with children with neurological conditions to test the data collection configurations. In Phase II of this study, physical performance measures data will be captured using the recommendations emerging from the current study that explored the feasibility of various instruments and configurations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".