Understanding Physical Activity Facilitated by a Single Session of Robotic Walking for Children and Small Adults Living With Severe Mobility Impairments
Bibliographic record
Abstract
BACKGROUND: Physical activity has many benefits but can be hard to achieve for people living with severe mobility impairments. Robotic walking may be an effective way for these individuals to achieve physical activity. OBJECTIVE: The aim of this study is to characterize the physical activity performed by children and small adults with severe mobility impairments during a single session of robotic walking. METHODS: We conducted a series of single-session assessments to evaluate the heart rate response experienced by children and small adults with severe mobility impairments during overground walking with an untethered robotic walking aid designed for children (Trexo). Outcomes evaluating physical activity were the average percent heart rate reserve (%HRR) throughout the training session, the most intense minute of training, and the total time spent physically active (at >20% HRR). Nonparametric descriptive statistics are presented as median (25th-75th percentiles). RESULTS: Fifteen individuals (aged 5-24) unable to walk independently participated in this study. Participants using the robotic walker had an average %HRR during training of 30% (21%-35%) and reached 40% (30%-50%) HRR in the highest minute of training. Participants spent a median of 17 (10-27) minutes and 99% (57%-100%) of the robotic walking time physically active. CONCLUSION: This study demonstrates that robotic walking can facilitate at least light physical activity for children and small adults with severe mobility impairments. The results of this study can be used to inform future providers on the physical activity benefits of robotic walking.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".