Robotic Walkers for Children and Youth with Cerebral Palsy: A Review of Past Successes and Ongoing Advancement
Bibliographic record
Abstract
Each child with a disability deserves the support required to achieve their maximum physical potential, optimal participation and an active lifestyle throughout their lifespan.In this paper, we aim to highlight past successes, present work and share a vision for future advancements in robotic gait technologies that enable walking in children and youth with severe physical disabilities.We will summarize work which demonstrates how a robotic walker with a lift and body weight support system has shown great promise in mobilization, exercise and participation in youth and young adults with Cerebral Palsy (CP).This study was conducted in 3 phases; 1) a case study, 2) a pilot study evaluating mobility in a clinical setting, and 3) a study focusing on training and participation in a community setting.Secondly, we will describe the planned assessment of the Trexo Plus, a walker and wearable lower extremity robotic device combination, in younger children with significant physical involvement secondary to CP.This device allows precise repetition of normal gait patterns while controlling range of motion at the hip, knee and ankle.Lastly, we will highlight international innovative robotic work which has produced a novel robotic platform with a partial body weight support system (PBWS), active system for adapting hip height and an exoskeleton with joint motion control.A multimodal human-robot interface (MHRI) accompanies the unique walker system which assesses intent to move, posture and balance and gait patterns.This system has the potential to allow therapists to construct and precisely evaluate unique robot facilitated programming.There is a new generation of robotic devices providing the means for individuals with CP to walk independently.With a look to the future, we endeavour to evaluate and facilitate the development of this robotic technology as it is integrated into clinical care, activity enhancement and community participation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".