# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art
Smartwatches are commercially available, economically viable, and feature several sensors (accelerometer, gyroscope, magnetometer, etc. ), a touchscreen interface, and wireless communication functionalities. A smartwatch is worn on the wrist, providing an advantageous position for monitoring arm and hand movements. Numerous recent studies have concentrated on employing smartwatches to manipulate robotic arms, illustrating both the viability and benefits of this method. [PERSON] et al. [13] proposed a pioneering framework named \"iRoCo\" for intuitive robot control utilizing a single smartwatch. The smartwatch's inertial sensors are utilized to determine the three-dimensional pose of the wearer's arm within their system. The smartwatch functions as a wearable motion-capturing apparatus on the wrist. They developed a machine learning model to deduce the locations of the wearer's elbow and wrist from the watch data, producing a range of potential arm postures [13]. By integrating this with voice commands (recorded through the smartwatch's microphone or a connected device), the user can execute intricate teleoperation activities. In use-case demonstrations, an operator might promptly assume the control of a robot by moving their arm--such as modifying the robot's carrying trajectory in real time--and thereafter revert to autonomous mode [13]. The ability to \"intervene anytime, anywhere\" with a simple wrist movement and verbal command illustrates the effectiveness of smartwatch-based human-robot interaction (HRI). The authors reported a 40% reduction in pose estimation error compared to previous state-of-the-art methods, yielding an approximate mean error of 2.56 cm in wrist placement [13]. This accuracy is significant given that only a single smartwatch is employed; it demonstrates the effectiveness of their refined learning approach and maybe the quality of modern smartwatch sensors. Smartwatches can identify both general arm orientation and subtle movements or contextual data. Numerous commercial smartwatches, for instance, provide touch, double-tap, and rotating movements (employing the bezel or crown). These inputs can be employed to control mode transitions or incremental motions of a robot. In a 2025 study published in _Applied Sciences_, [PERSON] et al. developed a smartwatch-based monitoring and alert system for plant operators [3]. The emphasis on worker monitoring (vital signs, movements, etc.) rather than direct robot control highlights that smartwatches are suitable for industrial settings, capable of connecting with cloud services and enduring prolonged usage throughout the day [3]. A smartwatch that controls a robot might also record data, such as the frequency of specific gestures, and interact with Industrial IoT systems for analytics or safety assessments. Our experimental setup corroborates the feasibility of employing a consumer smartwatch as a robotic controller. The gyroscope measurements of the watch are correlated with joint velocity commands for a compact industrial robotic arm within that system. The preliminary findings suggest that novices can maneuver the robot through fundamental movements (such as guiding an end-effector along a trajectory) following a brief orientation phase. This corresponds with the findings of [PERSON] et al. regarding smartphone interfaces and expands the application to a more compact wrist-mounted device [11]. The portability of smartwatches enables an operator to potentially operate a robot while moving freely on the shop floor, without the need for additional hardware. A technician may stand before a large robotic cell and use wrist movements to manipulate the robot for maintenance or programming, rather than using a tethered teach pendant. This may improve efficiency and ergonomics, as the individual's hands remain largely unobstructed. A technological concern for smartwatch-based control involves aligning sensor data with the robot's coordinate system.