# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
###### Abstract  
Accurate posture detection of elderly people is crucial to improve monitoring and provide timely alerts in homes and elderly care facilities. Human posture recognition is experiencing a great leap in performance with the incorporation of deep neural networks (DNNs) such as YOLOv9. Unfortunately, DNNs require large amounts of annotated data for training, which can be addressed by using virtual reality images. This paper investigates how to address the appearance domain that lies between synthetic and natural images. Therefore, four experiments (ViRTUAL-VIRTUAL; HYBRID-VIRTUAL; VIRTUAL-REAL; and HYBRID-REAL) were designed to assess the feasibility of recognising the postures of virtual or real elderly people after training with virtual and real images of elderly people. The results show that YOLOv9 achieves the most outstanding accuracy of 98.41% in detecting and discriminating between _standing_, _sitting_, and _lying_ postures after training on a large number of virtual images complemented by a much smaller number of real images when testing on real images. p 20  
However, to date, annotated datasets do not provide sufficient analysis of human activities, postures, or diversity of contexts [8]. In addition, privacy, legal, security, and ethical issues may limit the ability to collect sufficient human data. The use of human images in real-world scenarios is sometimes difficult due to privacy concerns. Given these issues, a popular approach is to feed the deep learning network with advanced forms of data, such as human skeletal joint trajectories, depth maps, or inertial data, instead of providing the network with raw data [9; 10; 11; 12]. Another popular approach is using Internet of Things (IoT) wireless sensing, like mobile sensors on phones or watches [13; 14]. Another alternative to real-world data that alleviates some of these problems is synthetic data [8]. Fortunately, the field of human posture recognition is experiencing a great leap in performance and capability due to the incorporation of deep neural networks [15; 16; 17]. Unfortunately, they require large amounts of annotated data for most applications. Acquiring adequate amounts of data is a tedious and expensive task. A powerful and cost-effective solution is to use synthetic data with automatically generated annotations [18]. In addition, domain adaptation is an elegant way to handle the lack of these datasets [19; 20; 21]. Finally, the use of synthetic images and/or videos generated by virtual reality software is becoming an attractive solution for training machine learning models in various application domains [22]. In particular, when focusing on human posture recognition, it is certainly important to have sufficiently large and varied image sets so that the models have a greater ability to generalise [23]. Recent studies have shown that synthetic images derived from simple 3D models can be used to train deep learning models to perform cross-domain synthetic-to-real visual recognition tasks [24; 25]. However, the performance of such cross-domain models is degraded due to the appearance domain gap between the real and synthetic images [26; 27]. In general, the appearance domain gap refers to visual differences between data from two domains caused by factors such as lighting, weather, time of day, and image resolution, among others. These differences can affect the performance of machine learning models, especially in computer vision tasks. Therefore, this work focuses on training the YOLOv9 model with synthetic and real videos to investigate the feasibility of later monitoring real elderly people and accurately identifying the three postures. Unity ([[https://unity.com/](https://unity.com/)]([https://unity.com/](https://unity.com/)), accessed on 15 May 2024 ) was chosen for its powerful and flexible 3D simulation capabilities, allowing us to create realistic environments and scenarios to generate synthetic data [28].