# Ergonomic Design of a Workplace Using Virtual Reality and a Motion Capture Suit
## 3 Description of Hardware and Software  
The hardware used for this study is divided into two parts: the hardware for the virtual reality and the hardware for motion capture technology. Virtual reality can be displayed in several ways. The most widely used option is an HMD (Head Mounted Display) [23]. The user is completely immersed in the virtual world, which helps them to comprehensively understand the projected situation. The HMD used in this study was the Oculus Quest 2. This is a standalone device (no additional hardware is required) that uses inside-out tracking; cameras placed on the front of the goggles track the controllers/hands and the surrounding environment, which can transmit to the virtual environment. At full load it can be used for 2 to 3 h. This device was chosen mainly because of its high mobility and display quality. The controllers can be used to provide feedback to the user by vibrations that the user feels, for example, when colliding with other objects. The MoCap suit Perception Neuron Studio from Noitom was used for the ergonomic evaluation. This type of suit for recording MoCap data was chosen mainly due to its ease of deployment for the user, customer support, and lower cost compared to its competitors. Gloves can be used with the suit to precisely track individual fingers. Perception Neuron Gloves were replaced by Oculus Touch controllers, primarily due to the ability to provide feedback to the user. The Perception Neuron suit consists of seventeen inertial measurement units (IMUs), and each sensor is composed of a three-axis accelerometer, three-axis gyroscope, and three-axis magnetometer that sense individual body parts. The sensors are mounted on flexible strips at precisely defined positions. The individual sensors send signals to the Axis Studio software (Noitom), which displays them on an animated avatar. The suits collected motion data at a 90 Hz sampling frequency. The recorded animations can be played back and exported. The Unity3D game engine was chosen to evaluate the recorded movements. Unity is a multiplatform game engine developed by Unity Technologies. It is primarily used for developing computer games for PCs, consoles, mobiles, and the web. Unity3D has also proven to be a reliable way to solve many practical and scientific problems, such as [33; 35]. Unity is a complete software application for creating 3D or 2D environments, creating animations, and interacting with models. It provides cross-platform connectivity with Windows, IOS, WebGL and, thanks to the collaboration with Android Studio, Android apps. The collaboration with Android Studio is at a high level, as evidenced by the fact that in recent years this engine has focused more and more on mobile apps and VR applications. In the last few years, this SW has started to expand into sectors other than just gaming. Unity itself is very complex when it comes to working with other programs (e.g., 3 DS Max, Blender, CAD software, etc.). Part of the support for official and community development is the Unity Asset Store. This is an online library of individual components and finished solutions. For our study we used the Oculus Integration and VRTK 3.3 assemblies to ensure the basic functionality of all the objects. In order to evaluate the recorded motions, it was necessary to develop a custom application that can play back and subsequently evaluate these motions. This application evaluates the recordings that are pre-recorded using MoCap technology. The actual software solution is a de facto parser/converter that processes the raw data from the measured sensors (FBX format from Axis Studio) and evaluates them according to the set parameters corresponding to the given legislation and processes and exports them to.csv format. As large volumes of data need to be processed, we had to optimise the import and export algorithms several times during development. To ensure a higher level of user control, the measured data can be visualised, including animations. Figure 2: Workflow diagram.