GeoFatigue: Outdoor Manual Task Fatigue Data Across Elevation Transitions

40 participants performed a standardized outdoor manual material handling task across three elevation-transition typologies: a flat trail, a stairway, and a ramp. The dataset includes wrist-worn physiological signals, GPS trajectories, fatigue ratings, and geospatial representations like a 3D point cloud. It was authored by Mohammadi Jahromi, Mahnoush and last updated on 2026-04 25.

Use Cases

• Modeling physical fatigue development based on integrated physiological and GPS trajectory data.
• Analyzing the impact of elevation transitions on worker performance based on data from flat, stairway, and ramp environments.
• Fusing multimodal sensor data for context-aware machine learning analysis in outdoor positioning constraints.
• Advancing spatial ergonomics research using geospatial representations like semantic GeoJSON polygons and 3D point clouds.

Strengths

• Data from 40 participants provides a basis for analysis.
• Includes multiple data modalities: physiological signals, GPS, fatigue ratings, and high-resolution 3D geospatial data.
• Task was performed across three distinct elevation-transition typologies with load and duration held constant.

Limitations

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

Provenance

Source

Borealis Harvested Dataverse

Collection Method

Collected from 40 participants performing a standardized outdoors manual material handling task with wearable sensors and geospatial mapping.

Time Range

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Freshness

Last updated 2026-04-25 04:13:34; freshness should be verified.

Geography

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