EMG and EEG Sensor-Based Exoskeleton for Knee Injury Rehabilitation

The paper discusses a new knee-surgery recovery-lower-limb exoskeleton system. Its novelty lies in real-time, patient-specific feedback. It carries sensors checking on muscle and brain activity. This is what lets the device vary in its support according to what the user feels. A machine learning tool can notify the differences between "pain" and "no pain," thus making the changing highly individualized and yielding better results in recovery for this purpose. The exoskeleton is designed with the MG995 servo motor, that will provide precise movement and also an Arduino UNO processor in efficient control. In comparison with other rehabilitation exoskeletons, it can be seen that the user makes use of a special method employing two types of sensors to detect pain better, well-suited for comfort of the patient. While preparing EMG and EEG signals, there are several steps taken to clean the noise; moreover, the data is normalized to aid the classifiers. Such techniques proposed may result in a higher sensitivity and specificity in the evaluation of pain. It uses advanced classification biofeedback techniques that shape up a new standard in adaptive exoskeletal rehabilitation, primarily in cases of knee rehabilitation.