# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 3 Methodology and Infrastructure  
This study is to assess the efficacy and user-friendliness of a smartwatch-operated control interface for an industrial robotic arm. The system comprises a smartwatch integrated with inertial sensors (a gyroscope and accelerometer) and a touchscreen; Python programming for data acquisition and analysis; a real-time communication platform (ThingWorx), and an xArm robotic arm. The sensor data are analyzed as motion and orientation metrics to perform robotic activities. Although various commercial gesture recognition solutions exist--such as the Doublepoint Kit [24] (Doublepoint, Helsinki, Finland)--this study did not employ any proprietary or closed-source packages. Instead, we used the Xiaomi Smartwatch 2 Pro (Xiaomi, Beijing, China), which offers built-in inertial sensors (IMUs) and runs Wear OS, combined with TouchSDK and WoW Mouse, both offered by Doublepoint, a flexible, platform-agnostic development kit. TouchSDK was selected for its ability to provide direct access to real-time IMU data, making it suitable for integration with Python-based robotic control systems. This approach ensured enhanced system adaptability, interoperability among smartwatch models, and superior reproducibility, irrespective of proprietary engines or vendor-specific algorithms. The system uses Doublepoint's TouchSDK open-source to access real-time IMU data from the smartwatch. WowMouse provided fast integration during prototyping, while gesture recognition and signal processing are handled in custom Python scripts. The architecture remains modular and can be adapted to other smartwatch platforms or gesture engines. Figure 1 illustrates the unprocessed output of the smartwatch's inertial measurement unit (IMU), depicting the temporal progression of rotational data over all three axes: X (blue), Y (green), and Z (orange). These signals indicate the angular velocities or orientation changes seen during a gesture-based robotic control session. The real-time analysis and interpretation of inertial sensor information are essential for the efficacy and dependability of gesture-based robotic control systems. Multiple critical factors substantiate the imperative for ongoing surveillance:  
1. Real-Time Gesture Recognition Fluctuations in angular velocity, especially sudden spikes along particular axes (e.g., Z-axis), facilitate the recognition of deliberate actions such as \"rotate,\" \"tilt,\" or \"tap.\" Each identified motion can be correlated with predetermined robotic commands, such end-effector rotation or gripper activation. Precise real-time interpretation guarantees a responsive and intuitive interaction experience between humans and robots. 2. Verification of Sensor Fusion and Filtering Algorithms Raw IMU data are intrinsically noisy, exhibiting significant instability on axes affected by dynamic user movement, often X and Y. Evaluating these signals prior to and after filtering facilitates the assessment and refinement of sensor fusion algorithms--such as the complementing filter or Kalman filter--employed to stabilize input and enhance orientation estimation precision. 3. Characterization of the Dominant Axis Sensor monitoring enables the determination of the most reactive axis during gesture execution. In observed trials, the Z-axis demonstrated strong amplitude and low noise characteristics, indicating its appropriateness for rotation-based control commands. This understanding facilitates the enhancement of command mapping and strengthens the control strategy's robustness. 4. Calibration and System Debugging Real-time signal tracking allows developers to identify and correct mismatches between input gestures and robotic responses. Such monitoring is essential for validating control loop integrity, reducing latency, and ensuring operational safety in industrial environments. Figure 1: Raw angular velocity data recorded by the smartwatch IMU during a gesture input session.