Preliminary Study on a Novel Protocol for Improving Familiarity with a Lower-Limb Robotic Exoskeleton in Able-Bodied, First-Time Users
Bibliographic record
Abstract
Lower-limb exoskeletons have been created for different healthcare needs, but no research has been done on developing a proper protocol for users to get accustomed to moving with one. The user manuals provided also do not include such instructions. A pre-test was conducted with the TWIN (IIT), which is a lower-limb exoskeleton made for persons with spinal cord injury. In the pre-test, two healthy, able-bodied graduate students indicated a need for a protocol that can better prepare able-bodied, first-time users to move with an exoskeleton. TWIN was used in this preliminary study and nine users were divided to receive a tutorial or no tutorial before walking with the exoskeleton. Due to COVID-19 regulations, the study could only be performed with healthy, young-to-middle-aged lab members that do not require walking support. The proposed protocol was evaluated with the System Usability Scale, NASA Raw Task Load Index, and two custom surveys. The members who received the tutorial found it easy to follow and helpful, but the tutorial seemed to come at a price of higher perceived mental and physical demands, which could stem from the longer testing duration and the need to constantly recall and apply the things learned from the tutorial. All results presented are preliminary, and it is recommended to include biomechanical analysis and conduct the experiment with more participants in the future. Nonetheless, this proof-of-concept study lays groundwork for future related studies and the protocol will be adjusted, applied, and validated to patients and geriatric users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".