Arm pointing movements in a three dimensional virtual environment: Effect of two different viewing media
Bibliographic record
Abstract
Virtual reality (VR) is being used increasingly in many fields of medicine, including rehabilitation. Both 2D and 3D virtual environments (VEs) can be viewed either through a head mounted display (HMD) or on a screen (computer monitor and rear projection system, SPS). However, the question of whether the medium through which the environment is viewed affects motor performance has not been addressed. The objective of our study was to determine whether movement patterns were different when movements were performed in a 3D fully immersive VE viewed via an HMD or SPS. Two groups of subjects were recruited (stroke, healthy). They performed pointing movements to targets placed in the ipsilateral, central and contralateral arm workspaces in a VE. The VE, designed to resemble the interior of an elevator, was viewed via an HMD or a SPS. Arm motor impairment and spasticity were evaluated in both groups of subjects. The kinematics of the pointing movements were recorded using an optical tracking system (Optotrak Certus, 100 Hz, 6 markers). Arm motor performance (speed, precision and trajectory straightness) and movement quality outcomes (elbow and shoulder ranges of motion and trunk forward displacement) were analyzed using 2 way ANOVAs. Preliminary results suggest that the control group had straighter movements and used more shoulder flexion as compared to the stroke group. When the VE was viewed via both media, there were no differences in terms of endpoint precision and speed, elbow and shoulder ranges of motion and trunk forward displacement in both groups. Both groups reported that they completely enjoyed performing the movements when viewing them via both media. All subjects in the control group and 80% of subjects in the stroke group reported that VE was engaging, that it felt real and that the movements performed were similar to those made in the physical world. The results of this study have implications for the design of rehabilitation applications using VR aimed at improving arm motor activity and function.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".