Wearable IMU Gait Data for Physics-Informed Fatigue Assessment

Data Sheet 1_Physics-informed hierarchical transformer for wearable sensor-based gait fatigue assessment.zip is a 2.0 MB dataset published by Kai Ding on figshare in April 2026. The dataset contains gait cycles from multiple participants, collected using wearable inertial measurement units (IMUs). It is intended for evaluating a framework that integrates biomechanical constraints with deep learning for multi-level fatigue classification.

Use Cases

• Training models for gait-based fatigue classification based on wearable IMU measurements.
• Developing physics-informed neural networks based on integrated biomechanical constraints like kinematic limits and energy conservation.
• Testing model robustness under sensor noise or individual sensor failures based on the described evaluation.
• Conducting cross-subject and cross-environment validation for field deployment based on the generalization results.

Strengths

• Dataset is 2.0 MB, facilitating quick download and initial exploration.
• Released under a permissive CC-BY-4.0 license, allowing for broad reuse and modification.
• Evaluation described includes tests for robustness under sensor noise and individual sensor failures.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset's small size (2.0 MB) suggests it may be a sample or contain limited records.
• Description metadata is limited; actual data quality and structure require manual inspection after download.

Provenance

Source

Kai Ding via figshare.

Collection Method

Gait cycles collected from multiple participants using wearable inertial measurement units (IMUs).

Time Range

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Freshness

Last updated 2026-04-13 05:30:58; freshness should be verified.

Geography

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