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

Submitted by Bob Researcher · Sports Science, Data Science · 2026-07-20

Approved

Basic Info

Department(s): Sports Science, Data Science
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.

Risk mitigations:

Data quality variability across partner clubs — Standardized data-collection protocol and training.

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%
Team Capacity Adequate: Yes

Justification: 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.

Review Outcome

Reviewed by Liam Manager on 2026-07-20 09:49.

"Approved -- strong industry partnership."