# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art  
The emergence of collaborative robotics has transformed the specifications for robot control interfaces. In traditional industrial environments, robots are isolated from humans and operated by experts, typically by offline programming or teach pendants. Cobots function alongside human workers, collaboratively sharing responsibilities such as assembly assistance, tool delivery, or material handling [5]. This tight engagement necessitates control systems that are rapid, adaptable, and secure, enabling an operator to modify robot movements instantaneously. Research shows that how easy a robot is to use is a key factor in getting people to use collaborative robots: the controls should make programming simpler and lessen the mental effort needed from human operators [1,5]. An evaluation of human factors in collaborative robotics suggests that intuitive interaction can improve productivity and safety in hybrid work environments [5]. Traditional control methods, such as teach pendants or keyboard/mouse teleoperation, are often cumbersome for novices and inadequately swift for dynamic collaboration. Programming even a basic motion with a teach pendant may necessitate accessing menus and coordinate frames, thus disrupting the workflow. In a human-robot partnership, a more intuitive method of expressing purpose is preferable, like how a human could gesture or physically direct a colleague. This has resulted in the investigation of interfaces that leverage inherent human communication methods: gestures, vocalization, and kinesthetic direction. Gesture-based control has garnered considerable interest due to its non-intrusive and intuitive nature, utilizing skills that humans naturally employ in everyday communication [6]. The ISO/TS 15066 technical definition for collaborative robot safety allows for the hand-guiding of robots, indicating that direct human interaction is permissible in collaborative environments [1]. Industry 4.0/5.0 frameworks emphasize the utilization of modern digital technologies (sensors, the IoT, and AI) to develop more intelligent and responsive automation [2]. Wearable devices adhere to this structure by operating as mobile sensor platforms that detect human movement or biological signals and swiftly relay that information to robotic Intelligent HMIs. Employing wearables allows individuals to remain \"in the loop,\" aligning with the human-centric automation goals of Industry 5.0 [4]. An article on Industry 5.0 highlights that the future of automation will entail a synergistic collaboration between humans and robots, with interfaces designed for natural interaction being essential. Thus, research on wearable and gesture-based control is not conducted in isolation; it is part of a broader effort to humanize industrial robotics and facilitate robot training to the level of any other colleague. Wearable human-machine interfaces provide an effective method for operating robots by detecting the operator's physical movements or gestures using body-mounted sensors. A range of wearable systems has been proposed, from instrumented garments equipped with several sensors to basic consumer devices such as smartwatches. The primary objective is to measure human movement in real time and translate it into commands for robotic control [7]. Unlike vision-based tracking, wearable sensors attached to the user are unaffected by line-of-sight limitations and lighting conditions, often providing high-frequency, low-latency data [7]. Wearables are particularly appealing for real-time teleoperation or collaboration in unstable industrial environments. An exemplary instance is the study of [PERSON] et al., who developed a wearable IMU system for flexible robot teleoperation [6]. Their system is equipped with multiple inertial measurement units (IMUs) attached to the operator's chest and arms, in addition to hand-mounted buttons.