Abstract: A wearable knee brace system integrates inertial measurement unit (IMU) and electromyography (EMG) sensors with machine learning models to prevent anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injuries. The system collects real-time biomechanical and neuromuscular data and analyzes the data using supervised, personalized, or federated learning techniques to identify high-risk movement patterns. Upon detecting elevated injury risk, the system may issue real-time alerts or activate a hybrid actuation system comprising high-force, low-displacement actuators and low-force, high-displacement actuators to reduce joint loading. The system further supports personalized model adaptation using calibration activities and transfer learning, as well as privacy-preserving performance improvements through federated learning. Feedback is provided through visual, auditory, or haptic interfaces and may be integrated with rehabilitation tools or mobile applications.

Abstract: A wearable knee brace system integrates inertial measurement unit (IMU) and electromyography (EMG) sensors with machine learning models to prevent anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injuries. The system collects real-time biomechanical and neuromuscular data and analyzes the data using supervised, personalized, or federated learning techniques to identify high-risk movement patterns. Upon detecting elevated injury risk, the system may issue real-time alerts or activate a hybrid actuation system comprising high-force, low-displacement actuators and low-force, high-displacement actuators to reduce joint loading. The system further supports personalized model adaptation using calibration activities and transfer learning, as well as privacy-preserving performance improvements through federated learning. Feedback is provided through visual, auditory, or haptic interfaces and may be integrated with rehabilitation tools or mobile applications.