# Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments
The person action annotation task with the environment is one of the resource-intensive tasks [24], while this sort of data is being increasingly applied in health care and tracking tasks, e.g., automatic fall detection systems [25, 26], with subsequent analysis of their causes to improve the quality and safety for people. Computer visualization and rendering provides the ability to generate a large amount of data with perfect labels [27], and the creation of a pipeline for generating a synthetic dataset based on human actions and interactions with the environment is an important and urgent task. The main emphasis in this research focuses on the usage of a simulation environment for applied tasks of human action predictions with the environment, including for further use in rehabilitation based on virtual reality. In the study, there is a high interest in applying the approach in the healthcare industry, particularly in regard to analysing the causes of falls. Because of the study, the life quality of people with disabilities [27] can be improved. The synthetic data generation pipeline [28] describing the consequences of a fall has serious limitations: evaluating what part of the body had been hit; usage of the dataset in the task of detecting the consequences of a fall; intentionality of the actions of persons in the reconstruction process of the fall. The dataset obtained using our approach has several differences from the existing synthetic options [20, 28, 29, 30]. For the more realistically based simulation, the digital human behaviour algorithm and inverse kinematics methodology were applied in the research. In [20, 22, 29], authors performed the human fall data generation without considering the inverse kinematics human model aspects, which is in contrast to our approach. In the existing works [28, 29], modelling scenarios are not sufficiently considered, and in the dataset [29] there is the dynamics of a person falling from an initial static state. We give a notice to produce the hit masks that indicate the places which have been hit, and it can be used in many applications, such as to analyse the causes of falls in the healthcare industry, in VR controllers design, etc. In [30], authors use a hit mask in some sense with the purpose of pressure assessment of bodies at rest on a mattress. Moreover, our dataset includes the diversity of clothes for simulated digital humans. Our research has put forward the following contributions:  
1. We implemented a physical model of digital humans to simulate the fall of a person and their interaction with the environment with inverse kinematics for producing various fall dynamics;
2. We took into account the coordinates of impact with the object of interaction in the process of registering a digital human fall to improve the segmentation ability of the hit mask;
3. We implemented the integration of digital human behaviour simulation for automatic \"playing\" of various scenarios of interactions and falls in a 3D scene automatically;
4. We apply the deep learning approach to examine the ability for training with synthetic data to recognize the real datasets. For the validation of the proposed synthetic dataset in recognition results, two classes were taken into account (fall and not fall). However, at the training stage, we have three categories that were labelled automatically using our generation pipeline: wall, floor and hit mask, obtained as an area of floor and body touch. The manuscript is structured as follows. In Section 2, we consider modern approaches to the definition of falling people. In Section 3, we present a modular approach to the problem of generating and collecting synthetic data from digital humans to study a person's fall and the consequences of a fall using the example of interaction with the environment. In Section 4, we present the implementation of the pipeline, the main results and the char acteristics of the generated dataset, discussing the applicability of the results and further development of the project for the application of virtual reality.