Funding
Funding not disclosed
Founders
Product
Problem
Individuals often rely on subjective feelings or generic training schedules to decide when to train, work, recover, or compete, leading to suboptimal performance and a higher risk of injury. Without objective, real‑time insight into their physical state, athletes and professionals cannot precisely time their activities for peak results.
Solution
Movement AI™ is a general‑intelligence platform that continuously monitors users’ physical activity through sensor data and applies machine‑learning models to interpret movement patterns. The system generates real‑time readiness scores that indicate when a person is optimally prepared to act, train, work, recover, or compete. By translating raw motion data into actionable insights, the platform helps users schedule high‑intensity efforts when they are most likely to succeed and schedule rest when injury risk is elevated. Users receive personalized recommendations via a cloud‑based dashboard or mobile app, enabling data‑driven adjustments to training plans, work routines, and competition strategies. The platform’s AI continuously learns from each user’s history, improving the precision of its alerts over time.
Target Audience
Primary customers include competitive athletes, fitness enthusiasts, coaches, and professionals such as military personnel or first responders who require precise timing for peak physical output and injury prevention.
Features
- Continuous data capture from wearable sensors (accelerometer, gyroscope, heart rate) for comprehensive motion profiling
- AI‑driven analysis that identifies patterns linked to performance readiness, fatigue, and injury risk
- Real‑time readiness alerts delivered via mobile app, smartwatch, or web dashboard
- Personalized training and recovery recommendations based on individual baseline and trends
- Cloud‑based analytics platform with visualizations of longitudinal performance metrics
- Integration APIs for popular wearable ecosystems and sports performance software
- Adaptive learning models that refine predictions as more user data is collected