# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 6 Results and Discussion
\(\bigcirc\) Middle: Y-axis movement. \(\bigcirc\) Bottom: Z-axis movement. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Timestamp** & **X\_R** & **Y\_R** & **Z\_R** & **X\_W** & **Y\_W** & **Z\_W** \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline  
* The right column shows corresponding real-time bar indicators for axis-specific values: \(\bigcirc\) & Top: Watch current vector values (X, Y, and Z). \(\bigcirc\) & Bottom: Robot current vector values (X, Y, and Z). This visualization reflects the correlation between smartwatch input and robotic response in real time. The near-synchronous fluctuation patterns across the axes suggest effective mapping and communication between the wearable IMU and the robotic control logic. Differences in magnitude and delay are minimal, confirming successful data acquisition, processing, and actuation within the integrated system. The display also provides diagnostic value during system calibration and gesture testing. A preliminary user study was conducted to evaluate the real-world usability of the smartwatch-based robotic control system. Ten participants (aged 22-31), none of whom had prior experience with robotics or wearable interfaces, were recruited for the test. After a 5 min guided tutorial, users were asked to perform a sequence of three predefined robot control tasks using wrist-based gestures. All participants were able to complete the tasks successfully, requiring on average fewer than three attempts per gesture to reach consistent execution. Qualitative feedback was gathered through short interviews, with users highlighting the system's intuitiveness, low cognitive load, and physical comfort. The use of familiar consumer hardware (the smartwatch) contributed to rapid adaptation and positive user acceptance. These results support the potential applicability of the proposed system in assistive and collaborative industrial environments and lay the groundwork for more comprehensive human-centered evaluations in future work. To control the robot, a predefined set of discrete wrist gestures is used. These include the following:  
* Single tap: starts or stops the robot's following behavior (also serves as a general stop command). * Double tap: toggles the gripper (open/close). * Lateral wrist flick (X-axis movement): triggers left/right motion. * Wrist tilt forward/backward (Y-axis movement): initiates forward or backward motion. * Wrist rotation (Z-axis): controls vertical translation (up/down). These gestures are recognized using thresholds applied to IMU data (the accelerometer and gyroscope) and are not based on continuous positional tracking. This allows for robust gesture recognition regardless of user position or orientation, minimizing false triggers from natural arm movement. Table 2 summarizes the mapping between gestures and robot actions. The quantitative results presented in Table 2 confirm the feasibility of utilizing smartwatch-based gesture recognition for real-time robotic control. Among the evaluated gestures, the two-finger tap demonstrated superior performance, achieving the lowest average latency and the highest precision, which highlights its robustness and suitability for time-critical tasks. In contrast, lateral movements along the X-axis exhibited greater variability, primarily due to increased sensitivity to inertial sensor noise and the presence of \begin{table}
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**Gesture Type** & **Average Latency (ms)** & **Precision (\%)** & **Observation** \\ \hline Wrist Rotation & 180 & 89.5 & Most consistent results \\ Two-Finger Tap & 90 & 94.2 & Fastest and most accurate \\ Lift (Y-axis) & 220 & 85.6 & Some drift observed \\ Side Move (X-axis) & 250 & 81.3 & Affected by sensor noise \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation of gesture types. involuntary micro-movements.