Design Considerations for the Development of Lower Limb Pediatric Exoskeletons: A Literature Review
Bibliographic record
Abstract
Cerebral Palsy is the most prevalent cause of gait disorder in childhood, affecting the range of motion, power, and joint torques of children. Several treatments are available, ranging from physical therapy to surgery. However, these treatments are usually complex, costly, and long. Robotic exoskeletons could provide longer, more frequent, and personalized training sessions with quantified data on the gait characteristics. Unfortunately, very few pediatric exoskeletons are available compared to those for adults. Therefore, design guidelines are needed for the development of pediatric exoskeletons to facilitate market entry. This article proposes design considerations through an in-depth review of the available pediatric lower-limb exoskeletons. This research has identified nine exoskeletons with at least one actuated joint at the ankle level and discussed their clinical, mechanical, and control characteristics. Although all the identified exoskeletons use electric motors to reduce their weight, improvements must be made to further minimize it. In addition, these exoskeletons need to be more easily adaptable to the user’s morphology. Impedance control methods are commonly used, which ensures the interaction safety. However, they should be more personalized to the specific neurological deficiencies. Furthermore, stronger validation of these exoskeletons is required through clinical trials.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".