# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art
Multimodal interfaces, which incorporate voice, gesture, gaze, and more inputs, are particularly relevant in collaborative settings. Voice commands function as a complementary augmentation to gesture control; explicit spoken directives can elucidate gestures or provide additional parameters (e.g., articulating \"place it there\" while pointing). [PERSON] et al. demonstrated the effectiveness of a zero-shot voice and posture-based human-robot interaction paradigm, emphasizing the integration of speech and body language for intuitive communication, employing extensive language models to interpret high-level commands [13]. In industrial settings, an operator may issue a command such as \"next step\" accompanied by gestures, and the system utilizes both signals to execute an action. Smartwatches can enable this by providing a constantly accessible microphone for the user, transmitting voice in conjunction with motion data [13]. Augmented reality (AR) interfaces are pertinent as well. Augmented reality glasses or tablets can superimpose robot status or intended trajectories within the user's visual field, creating a potent interface when integrated with gesture input. An operator utilizing AR glasses may visualize a hologram indicating the robot's intended movement and, through a simple hand gesture detected by the glasses' camera, authorize or modify it. This pertains to wearable control, as the differences between \"wearable\" and \"vision-based\" technology blur in augmented reality; the glasses serve as wearable sensors on the head and may additionally track hand movements. The major goal is to achieve a seamless collaborative experience, allowing people to easily guide and receive feedback from the robot. [PERSON] et al. (2023) emphasize that a human-centric design of interfaces, considering ergonomic and cognitive attributes, is crucial for the successful integration of cobots into modern production [1]. Ultimately, we identify a significant convergence in wearable robotics: technologies like powered exoskeletons (which are robotic wearables) have been investigated for their capacity to assist humans and to regulate other robots. An operator utilizing an upper-limb exoskeleton might have their movements directly mirrored by a larger industrial robot, exemplifying master-slave control. This provides the operator with tactile feedback (via the exoskeleton) while remotely managing the slave robot. While exoskeletons are generally employed for augmentation or rehabilitation, the notion of utilizing one as a \"master device\" for a subordinate arm has been evidenced in research contexts [8]. While still somewhat impractical for industrial applications due to the weight and complexity of exoskeletons, it highlights that humans can be equipped to varying extents--from a basic smartwatch to a comprehensive full-body motion capture suit or exoskeleton--based on the required precision of control. In several industrial contexts, the lightweight method (smartwatches, armbands, and gloves) is preferred because of its practicality. A multitude of studies completed from 2019 to 2025 augment and elaborate on the improvements in smartwatch and wearable interfaces previously referenced. [PERSON] and [PERSON] [16] conducted a detailed review of gesture recognition systems used in human-robot collaboration (HRC), highlighting important technological issues in recognizing and classifying gestures in real-world industrial environments. Their classification of sensory techniques and categorization algorithms remains highly influential in HRC research. [PERSON] et al. [17] introduced gesture-based interaction for assistive manufacturing, utilizing depth sensors and machine learning to identify hand and body movements for executing robotic orders. Their findings highlighted that consumers favor non-contact, natural methods, especially in restricted environments. [PERSON] et al. [18] introduced a novel zero-shot learning approach utilizing posture and natural voice inputs analyzed by big language models.