Emotional states affect steady state walking performance
Bibliographic record
Abstract
Gait is a large component and indicator of health. Many factors affect gait including age, disease, and even mood disorders. Few studies have looked at the influence of emotional states on gait. This study aimed to investigate the influence of emotional states on walking performance to understand whether an emotional state may be an important factor to consider when evaluating gait. Thirty-six young adults were recruited (23F, 13M) and performed a neutral baseline condition of walking which included six passes of walking across an 8m walkway (a total of 48m of walking). Participants then completed 6 pseudo-randomized emotional state induction conditions while immersive 360-degree videos were used to induce the following emotional conditions: happiness, excitement, sadness, fear, and anger. Participants viewed the emotion elicitation videos using a virtual reality head-mounted display (HMD), then rated their emotional state using self-assessment manikins and walked (without the HMD) over a pressure sensor walkway. One-way repeated measures ANOVA and pairwise comparisons were used to examine differences in gait parameters across the emotional conditions. Participants walked with significantly reduced step length and speed during the sadness condition compared to the other emotional conditions and the neutral condition. Furthermore, participants adjusted the timing of their walking during the sadness condition and walked with significantly increased step, stance, and swing times compared to other emotional conditions, but not the neutral condition. Step time was significantly reduced during the conditions of excitement and fear compared to the neutral condition. Emotions may impact variety of gait parameters involving pace and rhythm, however have little influence on gait variability and postural control. These results indicate that perhaps the emotions of sadness and excitement should be taken into account as potential confounds for future gait analysis.
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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 | 0.001 |
| 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) | 0.002 | 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".