Result: Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch.

Featured Application: This research demonstrates a practical approach for controlling industrial robotic arms using smartwatches. The system captures gestures and orientation through integrated sensors and connects to robots using Python 3.12.2 APIs. It is particularly useful in environments that require mobility, minimal training, or quick deployment—such as education, maintenance, or small-scale production. This method supports Industry 4.0 and 5.0 goals by lowering the learning curve and promoting human–robot collaboration. This paper presents a novel interface that leverages a smartwatch for controlling industrial robotic arms. By harnessing the gyroscope and advanced gesture recognition capabilities of the smartwatch, our solution facilitates intuitive, real-time manipulation that caters to users ranging from novices to seasoned professionals. A dedicated application is implemented to aggregate sensor data via an open-source library, providing a streamlined alternative to conventional control systems. The experimental setup consists of a smartwatch equipped with a data collection application, a robotic arm, and a communication module programmed in Python. Our aim is to evaluate the practicality and effectiveness of smartwatch-based control in a real-world industrial context. The experimental results indicate that this approach significantly enhances accessibility while concurrently minimizing the complexity typically associated with automation systems. [ABSTRACT FROM AUTHOR]

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