Inspiration

Sports injuries don’t usually happen suddenly—they build up silently due to poor biomechanics, unmanaged training load, and inadequate recovery. We were inspired by how most athletes realize the problem only after they are injured. Existing tools are reactive and generic. We wanted to build a solution that acts before failure happens, using only a smartphone, and makes elite-level sports science accessible to every athlete.

What it does

InjurEase is an AI-powered sports injury prevention app that predicts injury risk before it occurs. It analyzes athlete movement using smartphone-based biomechanics, monitors training load and recovery signals, and generates a daily injury risk score. Based on this score, the app provides personalized corrective exercises, recovery guidance, and pre-emptive alerts to help athletes train safely and consistently.

How we built it

We built InjurEase using a modular, AI-first architecture. The mobile app captures movement data through the phone camera and user inputs. Computer vision models perform pose estimation to extract joint angles and asymmetries. These signals, combined with workload and recovery data, feed into a hybrid injury risk engine (sports-science rules + machine learning). A cloud backend powers analytics, personalization, and adaptive AI coaching.

Challenges we ran into

One major challenge was converting complex sports science concepts into simple, actionable insights for users. Ensuring reliable biomechanics analysis from noisy camera data was another hurdle. We also had to balance model accuracy with real-time performance and build a meaningful solution within hackathon time constraints—without relying on expensive wearables.

Accomplishments that we're proud of

• Built a predictive injury prevention system instead of a reactive fitness tracker
  
• Implemented smartphone-based biomechanics analysis, removing the need for expensive sensors
  
• Designed a daily injury risk scoring engine with clear, explainable insights
  
• Developed an adaptive AI coach that personalizes corrective exercises in real time
  
• Integrated training load and recovery signals into a single risk model
  
• Created a scalable architecture suitable for individual athletes, teams, and coaches
  
• Delivered a working, impactful prototype within hackathon constraints

What's next for InjurEase

• Introduce coach and physiotherapist dashboards for team-level injury monitoring
  
• Enhance injury prediction accuracy using larger and more diverse athlete datasets
  
• Add wearable integrations (HRV, sleep, impact metrics) for deeper recovery insights
  
• Expand biomechanics analysis to sport-specific movements (cricket, football, athletics, etc.)
  
• Launch team challenges and competitive prevention programs to improve adherence
  
• Develop clinical-grade validation in collaboration with sports medicine professionals
  
• Scale InjurEase as a standard injury-prevention layer for athlete training ecosystems

Built With

• css
• geminiapi
• html5
• javascript
• nextjs
• reacthook
• shadecn
• tailwind
• typescript