Maintaining mobility in older age - design and initial evaluation of the robot SkyWalker for walking & sit-to-stand assistance
Bibliographic record
Abstract
Many elderly people suffer from a loss of mobility due to musculoskeletal disorders, neurological conditions, diabetes, frailty, or other impairments, and thus require assistance during walking and Sit-to-Stand (STS) transfers. Majority of commercially available mobility assistance devices are passive walkers, rollators, and canes which provide limited walking support and minimum support while standing up. Persons needing STS assistance typically rely on external forces applied by a person or a device to help them stand up. In this paper, we will present the design and functionalities of SkyWalker, a novel lightweight robotic rollator with active STS assistance. It is made for providing powered walking support on different terrains. STS support relies on a bilevel handle design and consists of active vertical lift and forward translation support during STS, then a handle change by the user to fixed handles once standing. Stand-to-Sit transfer follows the reverse order. To evaluate the SkyWalker design and control and identify areas of improvement, we conducted experiments with healthy subjects. A biomechanical study on STS motions compared 6 different STS trajectories for which the kinematic, kinetic, and user feedback were collected. Additionally, we tested SkyWalker's ability to support walking on different surfaces, on uneven terrain, and around obstacles. The study showcased the walker as a potential assistive device and identified limitations to be addressed prior to experiments with frail subjects. The data collected and the feedback from the subjects show great potential for the robot to be used as an assistive device in an indoor and outdoor environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".