# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art
[11] employed a smartphone's integrated sensors as a handheld controller for a robotic manipulator, focusing on users with disabilities. They developed three distinct smartphone-based interfaces: one utilized the phone's 3D orientation (tilt) to maneuver the robot along Cartesian axes, another employed the touch screen as a virtual joystick, and the third integrated orientation with touch inputs [11]. Their assessment of daily living activities, such as feeding and object retrieval, determined that smartphone control was intuitive and economical, enabling users to perform tasks with minimal training [11]. This demonstrates how readily available consumer wearables (smartphones, tablets, and watches) may be converted into efficient robot controllers. Wearable HMIs must prioritize user comfort and safety in their design. The extended or repetitive use of wearables should not cause fatigue or discomfort. Furthermore, any wireless wearable control must be dependable and secure to prevent inadvertent robotic movements. In collaborative environments, the system must consistently permit the human to override or halt the robot instantaneously--such as through a swift hand gesture or a button press on the wearable device (as demonstrated in [PERSON]'s system) [6]. The literature indicates a distinct trend: wearable interfaces enhance the naturalness and flexibility of robot control, while current research is advancing their precision (by improved sensors and fusion algorithms) and usability (via ergonomic design and minimal calibration) [7; 8]. Hand and arm movements serve as an innate mode of communication for humans and utilizing them for human-robot interaction (HRI) has become a dynamic field of study. Gesture-based robotic control can be achieved using vision-based systems (cameras seeing the user's motions) or sensor-based systems (wearables detecting gestures, as previously noted). Both techniques aim to streamline the robot training process to the simplicity of gestures or pointing, like giving orders to another person. Vision-based gesture recognition has advanced considerably owing to modern computer vision and deep learning techniques. High-resolution cameras and depth sensors, including Kinect and stereo cameras, enable robots to observe human counterparts and interpret their hand signals or body language. A recent comprehensive analysis by [PERSON] et al. [10] highlights that vision-based hand gesture recognition enhances natural and intuitive communication in human-robot interaction, effectively \"bridging the human-robot barrier\" [10]. These systems typically include hand detection in photos, the extraction of spatio-temporal features of gestures, and gesture classification by machine learning. In production environments, an employee may execute a designated hand signal (e.g., a thumbs-up or a stop gesture) that the robot's visual system identifies and reacts to. [PERSON] et al. [12] illustrated an egocentric vision methodology, wherein a camera affixed to the user (first-person perspective) transmits data to a 3D CNN (convolutional neural network) that identifies movements to modify a collaborative robot's actions [12]. Their solution enabled a worker to communicate with a proximate robot using naturally performed hand gestures from their perspective, resulting in dependable real-time recognition rates [12]. Vision-based solutions are advantageous as they do not necessitate the user to use any apparatus; yet they demand resilient computer vision capabilities in industrial environments, accommodating variable lighting, occlusions, and background interference. Gesture recognition is associated with the previously mentioned wearable human-machine interface (HMI) concern. Rather than continuously monitoring movement, several systems evaluate specific gesture commands obtained from wearable sensor data. A smartwatch or armband can recognize distinct gesture patterns, such as a double-tap or movement, and initiate preprogrammed robotic functions.