The results demonstrate the feasibility of developing affordable, real-time embedded systems for biocooperative control in robotic rehabilitation. A two-channel EMG system was successfully integrated into the RobHand exoskeleton, enabling accurate and responsive bilateral control with gesture detection rates exceeding 97%. Incorporating EMG-based visual feedback significantly improved user performance and self-regulation, as evidenced by reduced error distances in gesture tracking tasks. In addition, a multimodal wearable platform integrating electromyography (EMG), inertia measurement unit (IMU), electrocardiogram (ECG), galvanic skin response (GSR), and skin temperature (SKT) sensors was validated in virtual reality-based upper-limb therapy and assist-as needed wrist rehabilitation. The platform achieved good-quality signal acquisition, robust real-time processing, and reliable battery performance, demonstrating its versatility and strong potential for clinical application.