Surface electromyography (sEMG) can be effectively used to enable intuitive human–robot interaction with collaborative robotic systems such as the Franka robotic arm. In this context, sEMG sensors are placed on the user’s forearm or relevant muscle groups to capture electrical activity generated during muscle contractions. These signals are then processed in real time through filtering, feature extraction, and classification or regression algorithms to interpret the user’s intended motion or force. The decoded intent can be mapped to control commands for the robot arm, enabling functionalities such as gesture-based control, shared control, or teleoperation. For example, specific muscle activation patterns can correspond to movements like grasping, lifting, or directional positioning of the robot end-effector. Advanced implementations may incorporate machine learning models to improve robustness against signal variability and user differences. This approach allows for natural and non-invasive interaction, making it particularly suitable for applications in rehabilitation, assistive robotics, and human-in-the-loop manipulation tasks, where the safety and compliance features of the Franka robot further enhance usability.
