Virtual reality (VR) technology has gained popularity across various fields; however, its use often induces cybersickness, characterized by symptoms such as dizziness, nausea, and eye strain. This study investigated the differences in cybersickness levels and head movement patterns under three distinct VR viewing conditions: dynamic VR (DVR), static VR (SVR), and a control condition (CON) using a simulator. Thirty healthy adults participated, and their head movements were recorded using the Meta Quest 2 VR headset and analyzed using Python. The Virtual Reality Sickness Questionnaire (VRSQ) assessed subjective cybersickness levels. The results revealed that the SVR condition induced the highest VRSQ scores (M = 58.057), indicating the most severe cybersickness symptoms, while the DVR condition elicited significantly higher values in head movement variables, particularly in the coefficient of variation (CV) and integral values of head position along the vertical axis, and mean velocity (p < 0.05). These findings suggest that VR content characteristics directly influence users’ head movement patterns, closely related to cybersickness occurrence and severity. This study highlights the importance of analyzing head movement patterns in cybersickness research and provides insights for VR content design.

Vision and eye movements play a crucial role in maintaining postural stability. This study investigated the relationship between eye movements and postural control in healthy adults using mobile eye-tracking technology and posturography. Forty healthy participants underwent assessments of eye movements using a mobile eye-tracking system and postural stability using Tetrax posturography under various sensory conditions. Pearson correlation coefficients were computed to examine associations between eye movement parameters and postural control indices. Significant correlations were found between eye movement parameters and postural stability indices. Faster and more consistent horizontal eye movements were associated with better postural stability (r = −0.63, p < 0.05). Eye movement speed variability positively correlated with weight distribution indices under normal eyes open (r = 0.65, p < 0.05) and closed (r = 0.59, p < 0.05) conditions. Coordination of horizontal and vertical eye movements positively correlated with postural control (r = 0.69, p < 0.01). Negative correlations were observed between eye movement coordination and Fourier indices in various frequency bands (p < 0.05) and the stability index under different head positions (p < 0.05). The findings provide insights into sensory integration mechanisms underlying balance maintenance and highlight the importance of integrated sensory processing in postural stability. Eye movement assessments have potential applications in balance evaluation and fall risk prediction.

This study investigated the effects of different surface conditions on postural stability in response to unexpected perturbations. Thirty healthy adults underwent balance assessments on flat, incline ramp, balance pad, and balance pad on incline ramp surfaces. The center of pressure (COP) displacement in the mediolateral (ML) and anteroposterior (AP) directions, the velocity, and the area were measured. We found that the flat and ramp conditions resulted in significantly lower COP ML (F(3, 87) = 38.272, p < 0.001, ηp² = 0.569) and AP displacements (F(3, 87) = 89.177, p < 0.001, ηp² = 0.755), velocity (F(3, 87) = 89.177, p < 0.001, ηp² = 0.755), and area (F(3, 87) = 52.659, p < 0.001, ηp² = 0.645) compared to the balance pad and balance pad on ramp conditions (p < 0.05). The use of a balance pad, particularly on a ramp, significantly increased all the COP measurements, suggesting greater challenges to postural control. Through these findings, we demonstrate the adaptability and limitations of the human postural control system in response to varying surface conditions and perturbations.

Background: Knee pain may induce protective gait patterns that reduce knee loading, but the multi-joint trade-offs that emerge under uneven-terrain demands remain uncertain. Methods: Using SCONE/Hyfydy, we generated sagittal-plane predictive simulations over a deterministic ten-tile terrain sequence. A reflex-based controller was optimized under no knee-load limit (C0) or bilateral soft limits of 4, 3, or 2 body weights (C4, C3, and C2). We retained the lowest-objective solution from 24 runs per condition and reanalyzed every complete stance within 1–12 s by limb and terrain. Results: The C0 knee joint-reaction-force peaks of 8.35/8.25 BW (left/right) occurred during early stance on tile 9 (−4°) and tile 10 (−2°), respectively. The left peak arose in a stance confined to tile 9, whereas the right peak arose in a stance that crossed the tile 9-to-10 boundary; neither occurred during initialization. Corresponding maxima were 4.51/4.34 BW in C4, 3.63/3.71 BW in C3, and 3.60/3.73 BW in C2. Mean stance impulse decreased from 2.14/2.00 BW·s in C0 to 1.79/1.82, 1.65/1.69, and 1.58/1.59 BW·s in C4, C3, and C2, respectively. Evaluation-window mean hip loading increased and speed decreased, whereas initial loading rate did not change monotonically. The selected solution ranked first by objective in every condition; 5–17 of 24 runs were within 10% of the selected objective. Conclusion: Within this model, controller, weighting scheme, and single terrain traversal, knee-load limiting was associated with slower walking, lower stance-phase knee peaks and impulses, and hip-dominant redistribution. These results are scenario-specific model behaviors rather than direct clinical predictions.