KNEEGROW is an adaptive wearable biomechanics system designed to sense, interpret, and respond to lower-limb movement in real time. The system combines an MPU6050 IMU, ESP32, ESP-IDF, FreeRTOS, haptic actuation, and Raspberry Pi telemetry to continuously observe motion and provide closed-loop physical feedback. Beyond passive sensing, the system is being extended with a reinforcement-learning control layer that learns when the brace should intervene, when it should remain passive, and how its response should adapt to the user's movement and activity. This turns the brace from a fixed-rule rehabilitation device into a learning cyber-physical system capable of making real-time intervention decisions. The project explores wearable robotics, biomechanics, embedded systems, real-time control, reinforcement learning, sensor fusion, signal processing, and human–machine interaction, with the goal of building a wearable system that can understand movement and decide when to act.