Visual feedback and age affect upper limb reaching accuracy and kinematics in immersive virtual reality among healthy adults
Bibliographic record
Abstract
This cross-sectional study aimed to evaluate the effect of visual feedback, age and movement repetition on the upper limb (UL) accuracy and kinematics during a reaching task in immersive virtual reality (VR). Fifty-one healthy participants were asked to perform 25 trials of a reaching task in immersive VR with and without visual feedback of their hand. They were instructed to place, as accurately and as fast as possible, a controller held in their non-dominant hand in the centre of a virtual red cube of 3 cm side length. For each trial, the end-point error (distance between the tip of the controller and the centre of the cube), a coefficient of linearity (CL), the movement time (MT), and the spectral arc length of the velocity signal (SPARC), which is a movement smoothness index, were calculated. Multivariate analyses of variance were conducted to assess the influence of visual feedback, age and trial repetition on the average end-point error, SPARC, CL and MT, and their time course throughout the 25 trials. Providing visual feedback of the hand reduced average end-point error ( P < 0.001) and MT ( P = 0.044), improved SPARC ( P < 0.001) but did not affect CL ( P = 0.07). Younger participants obtained a lower mean end-point error ( P = 0.037), a higher SPARC ( P = 0.021) and CL ( P = 0.013). MT was not affected by age ( P = 0.671). Trial repetition increased SPARC ( P < 0.001) and CL ( P < 0.001), and reduced MT ( P = 0.001) but did not affect end-point error ( P = 0.608). In conclusion, the results of this study demonstrated that providing visual feedback of the hand and being younger improves UL accuracy and movement smoothness in immersive VR. UL kinematics but not accuracy can be improved with more trial repetitions. These findings could guide the future development of protocols in clinical rehabilitation and research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".