Project Card 09

Comparative Evaluation of Head Injury Criteria in Traumatic Impacts

Project Pathway

🟨 Data-Driven / Analytical Modeling

1. Background & Motivation

Head injury assessment in trauma biomechanics relies heavily on injury criteria derived from head kinematics, such as acceleration- and rotation-based metrics. Over the past decades, numerous head injury criteria have been proposed and adopted in different contexts, including automotive safety, sports concussion, and helmet evaluation.

Despite their widespread use, these injury criteria are based on different biomechanical assumptions and often lead to inconsistent injury predictions for the same impact scenario. Understanding when different criteria agree, when they diverge, and why is essential for responsible interpretation and application of injury metrics.

This project focuses on a comparative, data-driven analysis of head injury criteria, emphasizing biomechanical meaning, assumptions, and limitations rather than numerical simulation or experimentation.

2. Core Biomechanical Question

Under what impact conditions do commonly used head injury criteria provide consistent or conflicting injury assessments, and what biomechanical assumptions explain these differences?

3. Injury Mechanisms & Injury Criteria

The project should consider the following head injury mechanisms:

• Translational head motion
• Rotational head motion
• Duration and shape of acceleration pulses

Injury criteria to be considered may include (at least three):

• Head Injury Criterion (HIC)
• 3 ms criterion (a₃ms)
• Peak resultant acceleration
• Peak rotational acceleration
• Generalized Acceleration Model (GAM) or similar concepts

Students must explain:

• The biomechanical basis of each criterion
• The injury mechanisms each criterion emphasizes or neglects

4. Modeling / Analysis Approach

This is a data-driven and analytical project.

The student is expected to:

• Select representative head impact datasets from literature or open sources
• Analyze acceleration and/or rotational kinematic signals
• Compute multiple head injury criteria from the same data
• Compare injury predictions across criteria

No FEM or laboratory experiments are required.

5. Data Sources and Signal Processing

The project must include:

• Description of selected datasets (experimental, dummy, or simulation-based)
• Signal processing steps:
  • filtering,
  • integration (if applicable),
  • window selection
• Clear documentation of assumptions and processing choices

Students must discuss how signal processing choices influence injury metric outcomes.

6. Comparative Analysis

The project should include a structured comparison, such as:

• Agreement and disagreement between criteria
• Sensitivity to pulse duration and magnitude
• Sensitivity to rotational vs translational motion
• Scenarios where criteria may fail or overpredict injury

Visual comparison (plots, tables) is strongly encouraged.

7. Interpretation, Validation & Limitations

The project must explicitly discuss:

• Biomechanical interpretation of observed differences
• Comparison with reported injury risk thresholds in literature
• Limitations related to:
  • dataset selection,
  • absence of biological validation,
  • criterion-specific assumptions

Students must clearly state what conclusions are justified and what are not.

8. Feasibility & Reproducibility

The project must address:

• Software used (e.g., MATLAB, Python, spreadsheet tools)
• Computational simplicity and reproducibility
• Transparency of analysis steps

The project should be fully reproducible using commonly available tools.

9. Expected Outcomes

By the end of the project, the student should deliver:

• A comparative evaluation of multiple head injury criteria
• Identification of conditions under which criteria agree or diverge
• A biomechanically grounded interpretation of differences
• Recommendations for responsible use of head injury metrics

The outcome should demonstrate analytical maturity and critical thinking.

10. Deliverables

1. Final Report (20-25 pages, excluding appendices)
2. Processed data plots and comparison figures
3. Injury metric tables
4. Oral presentation (15-20 minutes)

Optional appendices:

• Data processing scripts
• Raw datasets
• Supplementary analyses

11. Project-Specific Grading Rubric

12. Project Scope Agreement

By choosing this project, the student agrees to:

• Focus on interpretation and comparison, not proposing new criteria
• Clearly document all assumptions and processing steps
• Avoid overstating injury prediction accuracy

Note:
Understanding why injury criteria disagree is often more important than computing their values.