# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 6 Results and Discussion  
Results showed consistent gesture recognition with minimal latency. Rotational gestures using gyroscope data had a delay under 200 ms, while tap gestures were executed almost instantly. This confirms the system's ability to support real-time robotic control based on wrist motion. Sensor drift in Inertial Measurement Units (IMUs), particularly over extended durations or dynamic motions, leads to degraded orientation accuracy. This can be addressed using sensor fusion algorithms such as Extended Kalman Filters (EKFs), complementary filtering, or periodic orientation resets based on a known reference position. Calibration challenges arise when mapping user gestures to robot commands due to individual variability in wearing position and biomechanics. To improve consistency, a startup calibration routine that includes pose alignment and guided gestures can be implemented. Battery life is a critical concern due to the continuous transmission of high-frequency IMU data. Energy efficiency can be enhanced through adaptive sampling (lower frequency during inactivity), motion-triggered transmission, and the use of industrial-grade smartwatches with extended battery capacities. To avoid unintended actions, the system applies gesture confirmation mechanisms such as minimum duration thresholds and optional double-tap inputs. These safety measures help distinguish deliberate commands from natural arm movements. To ensure safe operation, the system incorporates a dedicated stop gesture, which immediately halts the robot's motion upon detection. This acts as a software-level emergency interruption. Furthermore, the robotic arm used in the experimental setup includes a physical emergency stop button, as commonly found in industrial systems. Together, these features form a multi-layered fail-safe approach, combining gesture-driven and hardware-based safety mechanisms. The ThingWorx platform effectively visualized state changes and position tracking. Data from the experiment logs confirms accurate alignment between smartwatch motion and robotic action, with X_R, Y_R, Z_R, X_W, Y_W and Z_W showing closely matching values during synchronized gestures. Table 1 presents a sample of timestamped Cartesian coordinate data captured simultaneously from the smartwatch (columns marked \"W\") and the robotic arm (columns marked \"R\"), highlighting motion synchronization in millimeter precision. These are data logs exported from the ThingWorx platform, capturing real-time sensor values from both the industrial robot and the smartwatch. Each row corresponds to a timestamped event, showing the following columns:  
* X_R, Y_R, and Z_R: Real-time positional or orientation values along the X-, Y-, and Z-axes of the robot. * X_W, Y_W, and Z_W: Corresponding sensor values from the smartwatch along the same axes. This dataset is used to monitor and evaluate the synchronization accuracy between the operator's smartwatch gestures and the robot's response. It serves several key purposes:  
* Calibration verification: It allows developers to detect lag, mismatches, or drift between input and output. * System performance monitoring: It enables an analysis of sensor stability, noise, and gesture recognition reliability over time. * Debugging and improvement: The ability to log both device data streams in parallel helps optimize sensor fusion algorithms and data smoothing techniques. * Human-robot alignment tracking: It aids in understanding how effectively the smartwatch input translates into robotic motion, supporting both safety and precision in deployment scenarios. This level of real-time visibility makes ThingWorx a powerful diagnostic and analytics tool within the architecture of wearable-controlled robotic systems. Figure 9 presents synchronized real-time plots and bar graphs displaying motion data for both the smartwatch and the robotic arm along the X-, Y-, and Z-axes. * The left column shows time-series plots of positional or orientation data: \(\bigcirc\) Top: X-axis movement (red: robot and gray: watch).