Medical Device Based on a Virtual Reality–Based Upper Limb Rehabilitation Software: Usability Evaluation Through Cognitive Walkthrough
Bibliographic record
Abstract
Background: The use of virtual reality (VR) technology in rehabilitation therapy has been growing, leading to the development of VR-based upper-limb rehabilitation softwares. To ensure the effective use of such software, usability evaluations are critical to enhance user satisfaction and identify potential usability issues. Objective: This study aims to evaluate the usability of a VR-based upper-limb rehabilitation software from the perspective of occupational therapists. Specifically, the study seeks to identify usability challenges and provide insights to improve user satisfaction. Methods: The VR-based upper-limb rehabilitation software was tailored for therapists to operate while delivering therapy to patients. Usability testing was conducted with occupational therapists from the Korean National Rehabilitation Center using cognitive walkthroughs and surveys. Participants performed tasks that simulated real clinical scenarios, including turning the device on, assisting patients with wearing the device, and shutting it down. Observers recorded user reactions during task performance, and participants completed surveys to assess the ease of use of the user interface. This mixed-methods approach provided qualitative insights into user difficulties and their root causes. Results: Usability evaluations were conducted with 6 participants. Cognitive walkthroughs revealed potential areas for improvement in the software, including (1) enhancements to the graphical user interface for ease of use, (2) refinements in the natural user interface, and (3) better user manuals for clearer product instructions. The ease-of-use score for the user interface averaged 1.58 on a 5-point scale (1=very easy to 5=very difficult). Conclusions: This study provides valuable insights into improving user satisfaction by focusing on the needs of occupational therapists who operate a VR-based rehabilitation software. Future research should explore software refinement and clinical efficacy to maximize the therapeutic potential of such technologies.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".