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Daksh Vaidya
Independent Researcher
India
Abstract
This manuscript presents the design, implementation, and evaluation of a gesture-controlled robotic arm employing triaxial accelerometer sensors for intuitive human–machine interaction. The primary objective is to develop a real-time system that translates human hand gestures into corresponding robotic arm movements with minimal latency and high precision. The system architecture integrates a microcontroller-based processing unit, an accelerometer sensor module, wireless communication, and a five-degree-of-freedom robotic arm actuated via DC servomotors. Signal processing techniques including sensor calibration, filtering, and gesture recognition algorithms are applied to accurately map accelerometer outputs to joint angle commands. Two case studies illustrate system performance in pick-and-place and path-following tasks. Experimental results demonstrate an average gesture recognition accuracy of 95% and an end-to-end control latency below 120 ms. The proposed solution offers a cost-effective and scalable platform for applications in industrial automation, assistive devices, and teleoperation. Future work will explore integration of additional inertial sensors and advanced machine learning classifiers to enhance robustness under dynamic conditions.
Keywords
Gesture control, Robotic arm, Accelerometer sensors, Real-time processing, Human–machine interface
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