Rehabilitation for individuals suffering from motor disabilities caused by conditions such as spinal cord injuries, neuromuscular disorders, or stroke-related complications remains a significant challenge in both clinical practice and the daily lives of patients. As the global incidence of such conditions continues to rise, the need for effective and accessible rehabilitation solutions has become more urgent. In particular, the development of home-based assistive technologies, which enable patients to undergo therapy without frequent visits to medical centers, has become a key area of interest in biomedical engineering. Among these technologies, exoskeleton robots have emerged as promising tools for restoring lost motor functions. Recent advancements have shifted from predefined motion execution toward intelligent systems capable of recognizing the user's movement intentions. This study presents the design and implementation of a wrist exoskeleton prototype controlled by electromyographic (EMG) signals. The system uses an Arduino microcontroller integrated with EMG modules to detect muscle activity in the forearm and drive a servo motor, enabling wrist movements such as flexion-extension and abduction-adduction. EMG signals were recorded in a controlled laboratory environment following standard motor task protocols. Signal preprocessing and movement classification were carried out using MATLAB, utilizing its serial communication toolbox to interface with the Arduino board. The developed algorithm generates three-state control commands to drive the motor, allowing smooth, real-time imitation of voluntary wrist movements. The results demonstrate the feasibility of this approach for future application in wearable, intelligent rehabilitation systems tailored to individual users.