Human Navigation and Motion Tracking in Virtual Reality

Data from human subjects performing navigation tasks in a virtual reality environment. Raw positional data has been parsed into 300-millisecond samples for machine learning applications. The dataset includes object positions, human tracking data, and task instructions.

Use Cases

• Training machine learning models for human motion prediction based on parsed 300 ms samples.
• Analyzing obstacle avoidance behavior using object and human position tracking data.
• Studying path-following strategies in virtual environments from task instruction data.
• Benchmarking VR interaction techniques using combined target collection and navigation metrics.

Strengths

• Data is temporally structured for ML, parsed into consistent 300 ms samples.
• Includes multiple VR task modalities: target collection, obstacle avoidance, and path following.
• Originates from a known academic institution, The University of Texas at Austin.

Limitations

• No column names or detailed schema are provided, limiting reproducibility.
• Key metadata such as row count, file size, and last update date are missing from all sources.
• The dataset's scale and the number of human subjects are unknown.

Provenance

Source

Matthew H. Tong, The University of Texas at Austin

Collection Method

Collected from human subjects performing tasks in a virtual reality environment.