Simultaneous assessment of posture and saccadic eye movement with visual stimuli using head‐mounted display virtual reality technology in healthy older adults
Bibliographic record
Abstract
Abstract Background Postural sway and saccadic eye movement could be potential biomarkers to diagnose Alzheimer’s disease (AD) [1, 2]. We developed a new assessment system to evaluate posture and saccadic eye movement simultaneously in healthy older adults (OA) with visual stimuli of distractors, lower contrast, and stereopsis, which are also considered to be another deficit in people with AD [3‐5]. We hypothesised that more latency and errors in saccade would be observed in the condition with visual stimuli. Method 14 healthy older adults (71.9±4.8 years, 7 men / 7 women) joined the experiment after taking Montreal Cognitive Assessment (MoCA). The system consisted of stabilometer (GP‐5000, ANIMA Corporation, Japan) and a head‐mounted display virtual reality technology (VIVE Pro Eye, HTC Corporation, Taiwan). We measured both posture and eye movement simultaneously in the following test conditions: #1‐2) eyes‐open and closed without VR, #3‐4) pro‐ and anti‐saccade tests in 2D environment, and #5‐6) pro‐ and anti‐saccade tests in 3D environment. We developed the 2D environment for the baseline saccade measurement and 3D environment with more visual stimuli [6]. Each test took 60 seconds with 30 saccade trials. Result The participants achieved MoCA score of 27.5±1.6. We found significant difference in saccade latency between saccade types (mean: P < 0.001, SD: P < 0.001) but less significant difference between VR scenery (mean: P = 0.01, SD: P = 0.42). Moreover, we observed less significant difference in saccade error rate between saccade types (P = 0.07) and between VR scenery (P > 0.77). We did not see significant difference in postural sway (P > 0.05). Conclusion The results may imply that the healthy OA had sufficient attentional resources to both conduct the saccade tasks and maintain their posture in the designed VR environment with visual stimuli. Further research is required to evaluate the difference between OA with and without cognitive impairment. References: 1. Bahureksa, L., et al. (2017) 2. Wilcockson, T.D.W., et al. (2019) 3. Rizzo, M., et al. (2000) 4. Lee, C.N., et al. (2015) 5. Cunha, J.P., et al. (2016) 6. Imaoka, Y., et al. (2020)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".