# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 2 The State of the Art
The IMUs capture the orientation of the operator's upper body segments, and the operator's arm movements are converted into the end-effector motions of a collaborative robot in Cartesian space [6]. Activating the portable push button enables the user to toggle the robot's mobility, functioning as a safety precaution. [PERSON] et al. indicate that this wearable IMU interface facilitates the intuitive control of the robot; the operator merely moves as though directing the robot with an unseen handle, and the robot mimics the operator's posture [6]. This approach significantly reduces the necessary skill; an operator can \"instruct\" the robot on a trajectory through natural arm movements rather than programming waypoints. The wearable device was discreet and did not require precise sensor placement, making it appropriate for use on the shop floor [6]. Researchers have examined the amalgamation of diverse wearable sensor types to improve control precision. [PERSON] et al. [8] presents a lightweight exoskeleton-like controller utilizing three IMUs attached to the arm and ten potentiometers on the fingers, enabling the capture of up to 17 degrees of freedom in human arm-hand movement [8]. Their method figures out joint angles from IMU quaternions using a special technique called Relative Orientation Vector and converts human arm and finger movements into a robotic arm-hand model [8]. The incorporation of finger sensors facilitates the control of a robotic hand (gripper) alongside the arm, thereby enabling more intricate telemanipulation, such as gripping. They exhibited the precise mapping of human movements to the robot in real time, requiring just minimum calibration [8]. This highlights the trend of sensor fusion in wearables, which involves the integration of inertial, angular, or muscular sensors to comprehensively record the operator's intent. The myoelectric armband, typified by the Thalmic Labs Myo, represents a burgeoning wearable interface. Worn on the forearm, these devices employ electromyography (EMG) data from muscular activity, sometimes in conjunction with an inertial measurement unit (IMU), to detect actions such as arm movements or hand grasps. Studies demonstrate that Myo armbands can proficiently control robotic arms for basic actions. [PERSON] and [PERSON] [9] describe a system that utilizes wearable and contactless technology, where an operator's arm movements, captured by IMU/EMG, were used to control an industrial robot, augmented by a vision sensor for improved contextual awareness [9]. Multimodal techniques enhance robustness: wearable sensors deliver direct motion input, while external sensors, such as depth cameras, contribute to environmental awareness [9]. Although EMG-based control may be more complex to learn than IMU-based tracking, it enables the detection of the user's muscle commands prior to significant movements, potentially decreasing control latency. Moreover, EMG facilitates certain types of gesture identification without arm movement, such as identifying a hand clench to initiate an activity. This can enhance inertial data, particularly when the user's arm is stationary, yet muscle action is evident. Wearable interfaces encompass not just the monitoring of gross limb movements but also include wearable gloves designed for the capture of hand motions. Data gloves equipped with flex sensors or inertial measurement units can translate finger movements into commands for robotic grippers or decipher complex hand signals. A recent assessment demonstrates that glove-based gesture recognition can accurately capture 3D hand pose data irrespective of illumination conditions, making it optimal for human-robot interaction in manufacturing [10]. Nonetheless, gloves may be unwieldy if they are wired or hinder the user's typical hand functionality. Researchers are tackling this issue by employing lightweight, wireless gloves or conventional smartphones as wearable controls. [PERSON] et al.