Bibliographic record
Abstract
Given the risks involved with MW use, it is important to be able to gain the maneuvering and navigational skills in safe and controlled training environment. Training in a virtual reality (VR) setting allows for safe simulation of MW driving in a wide range of otherwise risky environments. In addition, it has been reported that the learned skills in a VR setting, can translate to real world scenarios, therefore allowing for skills improvement.The purpose of this thesis was to contribute evidence towards validating the usability and fidelity potential to which wheelchair skill training can be positively influenced by using a VR simulator, which provides realistic haptic feedback that mimics gravitational and inertial forces experienced during real propulsion. The first objective of this study was to validate the usability of the MiWe simulator by clinicians and expert MW users to, eventually, help improve wheelchair skills.The second objective was to determine whether or not haptic feedback would affect the overall experience by comparing the current study to a similar previous study done without haptic feedback. This thesis contains the results of an experiment that investigated the sense of presence, overall experience and ease of use of the experience. Sense of presence was broken down in four component that can be defined as level of involvement, experienced realism, spatial presence and overall presence. As for ease of use, it can be defined as level of ease faced when learning to operate and interact with the system, as well as the systems flexibility. Lastly, overall experience was assessed by a questionnaire and feedback on a multitude of factors such as comfort, control and level of difficulty experience.Our first hypothesis was that among clinicians and MW users, we would measure a positive view on ease of use and sense of presence during the MiWe simulator experience, and that there would be no difference between both groups. We were able to confirm this hypothesis with the results of the questionnaires and semi-structured interview. As for our second hypothesis, we hypothesized that the newer version of the simulator, which includes haptic feedback, would provide a more positive overall experience in comparison to our previous version with no haptic feedback. We were able to confirm that the inclusion of haptic feedback had a positive impact on overall experience. For this mixed method study, six MW users, as well as five expert clinicians in the field of wheeled mobility were recruited. They experienced a thirty minutes session in the McGill Wheelchair Simulator (MiWe) simulator. MW driving performance was assessed in MW users at baseline. After the experience in the MiWe simulator, three questionnaires were administered measuring sense of presence, ease of use and overall experience, as well as a semi-structured interview, to further investigate the overall experience. In terms of semi-structured interviews, the emerging themes were centered around technology adoption and branched into clinical usability and user experience. These findings intend to help rehabilitation professionals, who provide mobility-related services, to guide, alter and tailor their future mobility interventions according to their clients’ needs. To conclude, we were able to determine that among clinicians and MW users alike, there was a positive view on usability and sense of presence during the MiWe simulator experience, and that the addition of haptic feedback contributed significantly to the overall experience. The impact of our findings intends to help rehabilitation professionals, who provide mobility-related services, to guide, alter and tailor their future mobility interventions according to their clients’ needs
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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 | 0.018 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".