## 2 Related Work
### Devices for Capturing Visual Inputs
The use of pointing gestures is noted to be a more intuitive and natural approach compared to voice commands, as it requires only the user to indicate the target location without issuing a verbal command. However, practical application of pointing gestures presents two significant limitations. First, gesture recognition often involves complex image processing, leading to substantial computational demands. Second, the accuracy of pointing gestures can be insufficient, potentially causing the robot to navigate to an incorrect location. To address these issues, the authors proposed a hybrid control method, combining voice commands with pointing gestures, which they termed Voice-Guided Pointing Robot Navigation (VGPN). This method was successfully implemented and tested on the TurtleBot 2 robot platform, (Clearpath Robotics, by Rockwell Automation, Cambridge, ON, Canada) which was equipped with a 2D LiDAR and an RGBD camera. While the use of LiDAR for gesture recognition is relatively uncommon, the authors in article [36] proposed a system that leverages this technology. Their gesture recognition system comprises two primary components: body position estimation and gesture classification. The body position estimation module predicts the user's body position based on LiDAR scans, while the gesture classification module utilizes these estimated positions to identify the user's gestures. The position estimation system itself includes three key modules: 3D segmentation, 2D projection, and position estimation. In contrast, contact devices such as the sensor glove are specialized input devices designed to enable users to control robots or robotic systems through hand gestures and movements. These gloves are equipped with various sensors and technologies that detect and interpret the user's hand movements. Typically, glove-based systems require a wired connection and involve complex calibration and setup procedures [23; 25; 37; 38]. In the study presented in [39], the authors utilized sensor gloves to control the ABB Yumi assistance robot mounted on a mobile wheelchair. The gloves are equipped with finger bend sensors as well as other sensors--3-axis gyroscope, 3-axis accelerometer, and a digital motion processor. The control unit responsible for processing signals from the gloves is an Arduino Mega microcontroller (Arduino, Monza, Italy). The next device is the sensor band, which is designed to capture surface electromyography (EMG) signals from the muscles on the wearer's arm. This device is typically fitted to the forearm or upper arm and is used to recognize hand gestures by analyzing EMG data [23; 40]. Paper [41] proposed the use of the CSEM SwissRanger depth camera for visual input recognition and integrated voice commands, delivered through a Bluetooth headset, to enhance the control system. This combined approach was applied to the iRobot PackBot mobile robotic platform, enabling the robot to follow the operator, navigate through doors, and interact with the operator using an audiovisual control method. Additionally, gestures can be detected using contact sensing methods, which involve sensors attached to devices such as sensor gloves or sensor strips. Inertial sensors, including gyroscopes and accelerometers, are integral components of MEMS (Micro-Electro-Mechanical Systems) technology. This technology is widely implemented in various consumer electronics, such as smartphones, tablets, gaming systems, TV remote controls, and toys. In the study reported in [42], the authors employed a device that facilitates robot control through hand gestures. This sleeve monitors arm activity using both an EMG sensor and an inertial measurement unit (IMU). The sleeve interface is capable of recognizing 16 distinct gestures and determining the orientation of the forearm. Using this technology, the authors controlled the QinetiQ-NA Dragon Runner 20 tracked robot (QinetiQ North America, Waltham, MA USA), which is equipped with a four-degree-of-freedom manipulator.