KFA provides physics‑traceable AI analytics that quantify the internal stress (“cost”) of athletic movements, delivering two scores—Movement Quality (CBS) and Injury Risk (BIV)—from any video, optical, LiDAR, wearable, or biometric input. By mapping force through the kinetic chain without markers or lab equipment, teams can identify hidden injury risk early and intervene with coaching, medical, or roster decisions, helping prevent costly breakdowns such as UCL injuries.
Funding
Funding not disclosed
Founders
Product
Problem
Current sports analytics tools measure only the external outcomes of athletic movements, such as velocity or spin, leaving teams blind to the internal biomechanical stress that leads to injuries. Without insight into the hidden “cost” of each motion, coaches and medical staff cannot identify early signs of overload, resulting in costly injuries like UCL tears.
Solution
KFA offers an auditable, physics‑traceable AI platform that converts any movement signal—video, optical, LiDAR, wearables, or biometric data—into two actionable scores: a Composite Biomechanical Score (CBS) for movement quality and an Injury Vulnerability Index (BIV) for injury risk. The system’s Force Analysis Engine quantifies joint torque, kinetic‑chain stress, and force sequencing across four phases (generation, transfer, application, absorption) without markers or laboratory equipment. A Cognitive Load Layer adds context on pressure, decision load, and fatigue, linking mechanical stress to mental factors. All conclusions are traceable to underlying physics, and the platform delivers tiered, dimension‑specific risk reports that enable coaches, medical staff, and front offices to intervene early and manage athlete durability.
Target Audience
Primary customers are professional and elite sports organizations—coaches, performance analysts, medical staff, and front‑office decision makers—who need precise, early‑warning insights into athlete injury risk and movement efficiency.
Features
- Multi‑modal input support (video, optical, LiDAR, wearables, biometrics) with no markers or lab setup
- Physics‑based AI engine that calculates joint torque, kinetic‑chain stress, and force sequencing across four biomechanical phases
- Cognitive Load analysis that incorporates pressure, decision‑making load, and fatigue into risk assessment
- Generates two transparent scores: CBS (movement quality) and BIV (injury risk), each broken down into 14 tiered dimensions
- Auditable data lineage linking every score and risk flag back to specific video frames and physical measurements
- Tiered risk visualization (Advantage, Manageable, Volatility, At‑Risk) for rapid decision‑making by coaching and medical teams