Gait and Balance Data from a Semi-Immersive VR Concussion Study

A proof-of-concept study evaluated a novel semi-immersive virtual reality dual-task protocol for identifying lingering postural control deficits in individuals with a remote history of concussion. The dataset, authored by Brent A. Harper and shared on figshare, likely contains measurements of postural sway and gait parameters under cognitive load. Statistical analyses included independent-samples t-tests, MANOVA, and Cohen’s d for significant effects.

Use Cases

• Training machine learning models to classify concussion history based on postural sway dynamics mentioned in the description.
• Analyzing the impact of dual-task cognitive challenges on gait parameters like stride length mentioned in the description.
• Comparing the sensitivity of central versus peripheral visual stimuli in detecting subtle post-concussion deficits as described.
• Developing more ecologically realistic assessment protocols for long-term concussion outcomes as suggested by the study.

Strengths

• Data was collected using an integrated system of motion capture and force plate technology for precise measurement.
• The study design includes a comparison between healthy controls and individuals with a documented history of concussion.
• Statistical results are reported, including specific t-test values and effect sizes (Cohen's d) for significant findings.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Collection Method

Data likely gathered from a controlled study using a semi-immersive VR system with motion capture and force plates.

Freshness

Last updated 2026-04-30 05:17:19; freshness should be verified