AI-Driven Biomechanical Injury Risk Prediction in Elite Athletes (AI-BIRP)

Basic Info

Department(s)

Sports Science, Data Science

Applicant Role

—

Primary Banner

Joint

Project Types

Research, Industry Collaboration

Legal Signature Required

Yes

Line Manager Approval Required

Yes

Supervisor Approval Required

No

Team Members

• Bob Researcher — Principal Investigator (Sports Science) · 40% effort

External Partners

• Elite Sports Analytics Ltd — Industry (Luxembourg) · contact: Dr. Partner

Classification

Funding Type

Competitive Grant

Strategic Alignments

AI And Technology, Injury Prevention

Research Pillars

Data, Collaboration

Expected Contributions

—

Summary

Abstract: This project develops machine learning models to predict biomechanical injury risk in elite athletes using multimodal sensor data collected over a full competitive season.

Objectives: Build a multimodal injury-risk prediction model; Validate the model prospectively with partner clubs

Methodology: Longitudinal data collection combining IMU, force-plate, and self-reported load data, analyzed with supervised machine learning.

Expected Outputs: Publications, Prototypes Tools

Keywords: machine learning; injury risk; elite athletes; biomechanics

Impact, Risk & Ethics

Scientific impact: Advances the state of the art in multimodal injury-risk modeling for elite sport.

Societal impact: Reduces injury burden and career disruption for professional athletes.

Ethics approval required: Yes — Approved.

Budget

Dedicated Budget

Yes

Total Budget

250,000.00 EUR

Requested Funding

200,000.00 EUR

Own Contribution

50,000.00 EUR

Funding Body / Sponsor

National Research Fund

• Personnel: 150,000.00 EUR — Postdoc + research assistant
• Equipment: 40,000.00 EUR — Sensor hardware
• Travel: 10,000.00 EUR — Partner club visits

Feasibility

PI statement: I have successfully led two prior externally-funded projects of similar scope and will dedicate 40% effort to this project.

Estimated PI Effort

40%

Justification for team capacity: The team combines biomechanics and machine-learning expertise required for this project.

Compliance & Governance

Data Management Plan Required

Yes

GDPR Considerations

Yes

Open Science Plan S Compliance

Yes

IP Considerations

Yes

IP description: Predictive model IP to be jointly owned per the partnership agreement.