Praevo develops a wearable device that continuously monitors athletes’ biomechanics and physiological signals to forecast injury risk before symptoms appear. By applying machine‑learning models to real‑time data, the system alerts users and coaches to potential issues, enabling preventive training adjustments and reducing downtime. The product is currently in early access, with a waitlist for early adopters seeking data‑driven injury prevention.
Funding
Funding not disclosed
Founders
Product
Problem
Athletes and coaches often lack real-time insight into biomechanical stressors that lead to injuries, resulting in unexpected downtime and shortened careers.
Solution
Praevo is developing a wearable device that continuously captures an athlete’s biomechanical data during training and competition. The device streams sensor measurements to an AI-driven analytics platform that identifies patterns associated with injury risk. Predictive models generate early warnings, allowing users to adjust technique, load, or recovery protocols before an injury manifests. By providing actionable insights in real time, the system aims to enhance training safety, reduce missed sessions, and extend athletic longevity.
Target Audience
Primary users are professional and elite athletes, as well as coaches and sports performance teams seeking data-driven injury prevention tools.
Features
- Integrated multi-sensor suite (e.g., inertial measurement units, pressure sensors) for continuous biomechanical monitoring
- Edge computing on the wearable for low-latency data preprocessing
- Cloud-based AI models that analyze longitudinal sensor data to forecast injury risk
- Real-time alerts delivered to a mobile app with recommended preventive actions
- Secure data synchronization and storage compliant with athlete privacy standards