# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art
The wristwatch application in our context, as described at the beginning of this assessment, enables fundamental gesture recognition, including two-finger taps to perform commands [3]. These subtle touch gestures on the wrist may facilitate mode changes or task validation in a human-robot collaboration setting. Compared to comprehensive skeletal tracking, recognizing a limited range of motions may be less computationally demanding and more resilient to noise, but this comes at the cost of expressiveness. As a result, several systems combine continuous control with gesture commands. [PERSON] et al. [13] integrated verbal commands with smartwatch-based arm posture monitoring, allowing users to manipulate the robot via arm motions in addition to verbal or tactile directives for actions [13]. This multimodal approach (voice + wearable motion) significantly enhanced the interface's adaptability, enabling \"anytime, anywhere\" interaction in the robot's operations by the human operator [13]. Dynamic free-form motions, such as waving an arm to divert a robot's attention or delineating a shape in the air, have been investigated. [PERSON] et al. [14] introduce a sophisticated human-robot interaction system in which an operator uses dynamic hand gestures to teleoperate a quadruped robot equipped with a manipulator [14]. Their technology uses a vision sensor to monitor a series of hand movements, rather than a static gesture, to control the robot's actions. They indicated that dynamic motions, analyzed by deep learning, facilitated the intuitive real-time remote control of a sophisticated robot (a bipedal machine with an arm) [14]. The system might, for example, read a continuous circular motion of the hand as \"rotate in place\" or an upward wave as \"stand up,\" illustrating the expressive capacity of dynamic gestures [15]. The problem resides in ensuring reliability; misreading a gesture may lead to unintended movement. Current research focuses on resilient, context-sensitive gesture recognition. [PERSON] and [PERSON] [16] investigated gesture recognition for human-robot interaction and highlighted the need for strong algorithms that can handle different ways people perform gestures [16]. Recent research utilizes advancements in deep learning, such as 3D CNNs and Transformers, and frequently integrates modalities, including vision and IMUs, to enhance accuracy [10]. An important element of gesture-based human-robot interaction is user training and ergonomics. Vision-based gesture control is highly user-friendly (requiring no wearable devices); nonetheless, users must familiarize themselves with the specific movements recognized by the robot and execute them distinctly. A specific lexicon of gestures exists, like sign language for robots. Certain studies endeavor to implement adaptive learning, enabling the robot to acquire personalized gestures from the user via demonstration. Conversely, gesture control with wearables can capitalize on more nuanced actions, such as a delicate wrist flick identifiable by an IMU, which a camera may overlook if the hand is obstructed. [PERSON] and [PERSON] (2021) developed a kinematic model of the ABB IRB 7600 robot, using Denavit-Hartenberg parameters and inverse kinematics methods to enable precise motion control [15]. Although presented as motion tracking rather than gesture commands, it obscures the distinction: the system \"recognizes\" the operator's arm movements (a trajectory gesture) and correlates them with the robot. They achieved a mean positional error of roughly 2.5 cm for the estimated wrist and elbow positions by optimizing machine learning models utilizing smartwatch sensor data [13]. This accuracy illustrates that inertial gesture tracking from wearables can be sufficiently precise for controlling industrial robot arms in diverse tasks, from coarse positioning to fine adjustments. Smartwatches have emerged as a notably intriguing platform for robot control among wearable gadgets.