Published Paper Details:

Human machine interface technologies have significantly advanced the development of assistive robotic systems. This paper presents the design and implementation of a hybrid control system for a robotic arm using electromyography (EMG) signals combined with voice commands. Surface EMG sensors are used to capture electrical signals generated by muscle contractions from the user's forearm. These signals represent the user's muscle activity and intention to perform certain movements. The EMG signals obtained from the electrodes are very weak and susceptible to noise. Therefore, they are first amplified and filtered using an EMG sensor module before being processed by an Arduino Nano microcontroller. The processed signals are analyzed to detect muscle contraction levels, which are then translated into commands to control the movements of the robotic fingers. However, relying solely on EMG signals can lead to limitations due to signal variability, muscle fatigue, and differences in electrode placement. To enhance system flexibility and usability, a smartphone-based voice command interface is integrated into the system. Voice commands are transmitted through a Bluetooth communication module (HC-05), allowing the user to control additional robotic movements through speech.The robotic arm consists of five servo motors that simulate the motion of human fingers. The Arduino Nano processes both EMG signals and voice commands to generate appropriate control signals for the motors. Experimental results demonstrate that the proposed hybrid control approach improves interaction efficiency, response time, and usability .This system provides a low-cost assistive solution that can be beneficial for individuals with upper-limb disabilities, rehabilitation therapy, and human-machine interaction research.