# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 5 Experimental Configuration
The IMU comprises an accelerometer, gyroscope, and optionally a magnetometer, facilitating multi-dimensional motion tracking and orientation estimation. Sensor fusion and signal processing algorithms were employed to extract the following components:  
* Linear acceleration from the accelerometer detected the device's movement relative to an inertial reference frame. Raw acceleration data along the X-, Y-, and Z-axes were subjected to low pass filtering to mitigate noise prior to integration into motion trajectories, facilitating the translational control of the robotic arm. * Angular velocity obtained via the gyroscope measured rotational motion around the roll, pitch, and yaw axes. These signals were also filtered exponentially and integrated to estimate relative rotation, especially useful for detecting Z-axis rotations and contributing to gravity vector estimation. * Gravity vector, computed as a software-based virtual sensor combining accelerometer and gyroscope data, allowed the extraction of device orientation relative to the vertical axis. A similar low-pass filtering approach was performed to stabilize the resulting vector, which was utilized to estimate \"up/down\" directions based on wrist posture. * Three-dimensional orientation, represented as a quaternion, was computed through fusion of accelerometer, gyroscope, and optionally magnetometer inputs. These quaternions were normalized and later converted into Euler angles for interpretation within the robot control logic. Figure 6: Spatial displacement in millimeters during lateral arm movement. The X- and Y-axes represent horizontal and vertical motion captured by the IMU. The data demonstrate variability and noise sensitivity in gesture execution. This 3D plot (Figure 7) illustrates the spatial trajectory and orientation of the smartwatch as interpreted during one complete gesture cycle. It plays a crucial role in the evaluation of spatial accuracy, motion stability, and system responsiveness. 1. Spatial Cogninance of the Gesture  
The blue line illustrates the entire motion trajectory in three-dimensional space, depicting the operator's hand movement inside a volumetric framework. This graphic verifies if the gesture stays inside anticipated space limits (e.g., secure working areas adjacent to the robot). 2. Orientation Mapping  
The use of quaternions to derive orientation axes (RGB) provides a robust and drift-resistant way of tracking rotational movement. This is critical for differentiating gestures that may have similar trajectories but differ in wrist rotation. 3. Current Position Feedback  
The red marker offers immediate visual feedback on the final position at the end of the gesture. This is useful for comparing start vs. end positions and for determining gesture completion. 4. Utility in Research  
This figure supports the development of more advanced sensor fusion techniques by combining orientation and position tracking. It also enables the evaluation of gesture precision and repeatability, two essential metrics in human-robot collaboration scenarios. Sensor fusion was achieved using algorithms like complementary and Kalman filters, which improved the stability and accuracy of orientation estimates. The TouchSDK pro  
Figure 7: Three-dimensional tracking of smartwatch movement using quaternion-based orientation. Blue line shows trajectory, red dot marks current position, and X-, Y-, and Z-axes show real-time orientation. Position is shown in centimeters. Useful for advanced fusion analysis. vided access to both quaternion and gravity vector data, allowing smooth integration into the robot control system. The diagram from Figure 8 illustrates the end-to-end data flow in smartwatch-based robotic control architecture. It begins with the Xiaomi Smartwatch 2 Pro, which captures raw sensor data--including linear acceleration, angular velocity, gravity vector, and 3D orientation (quaternion). These values are processed using internal filtering (low-pass and sensor fusion), generating clean, interpretable motion data.