## 2 Related Work
### Devices for Capturing Visual Inputs  
Technical devices used for data collection can be classified into two main categories. The first category encompasses non-contact devices, such as RGBD (depth D and color RGB) cameras and Light Detection and Ranging (LIDAR) systems. The second category includes contact devices, such as sensor gloves [23]. Popular video cameras, such as webcams and camera modules for platforms like Arduino, are well-suited for various applications. Research discussed in [24; 25; 26] explores the use of these cameras for hand gesture recognition. A notable variant of the conventional camera is the RGBD camera, which differs by including a depth sensor, thereby capturing both color and depth information. Another popular choice is depth sensor. It can capture depth information in addition to color or 2D image data. It measures the distance between the sensor and objects within the scene, enabling the generation of a three-dimensional representation of the environment. There are several types of depth sensors [27; 28; 29]:  
* Structured light depth sensors project a light pattern onto the scene and analyze the distortion of the pattern to compute depth information. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Coexistence** & **Cooperation** & **Collaboration** \\ \hline Work space & ✗ & ✓ & ✓ \\ Direct contact & ✗ & ✗ & ✓ \\ Work tasks & ✗ & ✗ & ✓ \\ Resources/material & ✗ & ✓ & ✓ \\ Simultaneous processes & ✓ & ✓ & ✓ \\ Sequential processes & ✗ & ✓ & ✗ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of different types of HRC based on various shared attributes. * Stereo depth vision sensors utilize two or more cameras with fixed distances between them. These sensors calculate depth by comparing discrepancies between corresponding points in the images. * Ultrasonic depth sensors can measure distance using sound waves and are particularly useful in applications where other methods may be impractical, such as in measuring liquid levels. * Time-of-Flight (ToF) depth sensors emit light or laser pulses and measure the time it takes for the light to return to the sensor, thereby determining the distance to the objects in the scene. In paper [30], the authors employed devices from Microsoft and Leap Motion to capture gestures. The Microsoft Kinect RGBD camera facilitated the recognition of body gestures, while hand gestures were processed using the Leap Motion sensor. The Leap Motion sensor provided precise 3D tracking of hands and fingers through the use of cameras and infrared sensors, making it suitable for applications in virtual reality, gaming, and 3D modeling due to its ability to enable intuitive and natural interactions. The paper proposed a method to streamline the programming of industrial robots by utilizing visual sensors to detect human movements. The authors created a dictionary of body and hand gestures that allows the robot to execute various directional movements. An application was developed on the robot to translate these gestures into corresponding movements. This method was integrated within an open communication architecture based on ROS. Additionally, the Kinect RGBD camera was also utilized by the authors in papers [31; 32] for gesture recording. Another applicable technology is LiDAR, a remote sensing technique that employs lasers to measure distances and produce high-resolution three-dimensional maps of the environment. LiDAR systems function by emitting laser pulses and measuring the time it takes for these pulses to return after reflecting off objects, thus enabling the generation of detailed and precise topographic data. LiDAR technology is utilized across various fields, including geography, geology, archaeology, autonomous vehicles, forestry, and urban planning [33; 34]. In paper [35], the authors investigated two control methods, i.e., voice commands and visual pointing, to navigate a robot to a desired position.