# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 5 Discussion  
Using YOLO for different human posture recognition tasks is not new. For instance, research with YOLOv5s [6] has highlighted improvements in fall detection, while YOLOv8 [32] has highlighted the potential of YOLOv8 in healthcare monitoring applications. The potential of YOLO models extends beyond fall detection. As suggested by studies of YOLOv4 and LSTM [41] and other advanced YOLO networks [42; 43], these models show promise in detecting different postures and activities, potentially aiding broader healthcare applications. Research efforts have also been directed at optimising YOLO models for better performance. Novel approaches such as GL-YOLO-Lite [44] indicate continued improvements in model accuracy and efficiency. In addition, recent developments in human posture detection, such as CHP-YOLOF and CHP-YOLOX [45], have extended the capabilities of these systems to a wide range of human activities. Our approach used YOLOv9 because it builds on the strengths of its predecessors and offers improvements in architecture and optimisation techniques. Although the core architectures of YOLOv8 and YOLOv9 are similar, YOLOv9 introduces refinements in the backbone and the head of the model to provide better feature extraction and more efficient handling of computational resources. These improvements are particularly relevant in scenarios that require high performance with minimal latency, such as real-time detection tasks. In addition, the adoption of YOLOv9 provided the opportunity to work with the latest developments in the YOLO framework, ensuring that the model benefited from the latest research and optimisations. In summary, while the specific task at hand could have been achieved with previous models such as YOLOv8, the decision to use YOLOv9 was driven by the desire to take advantage of the latest developments in the YOLO architecture, ensuring optimal performance and future-proofing the model. Specifically, our proposal used the YOLOv9 model to efficiently detect three important postures for monitoring elderly people at home. The authors recognise that classifying three postures to study human behaviour may seem limited. For example, a recent study [46] looked at seven activities in the ADLF (Activities of Daily Living and Falls) dataset, including standing, sitting, lying, and falling. Another paper [9] that includes standing, sitting, lying, and falling also classified seven postures. However, the classification of a smaller number of human activities has also been described very recently [47]. In that paper, the four postures of standing, sitting, lying, and dangerous sitting were studied. In our case, working on three widely studied postures is only a first step, and we will certainly expand our approach to classify a greater number of postures. However, we would like to emphasise that the main objective of this work was to study the possibility of bridging the gap in the appearance domain between real and synthetic data using YOLOv9. The current approach provided several important insights into the performance of the YOLOv9 model in different environments and with different datasets composed of synthetic and real images:  
* The performance of the model in recognising postures in the virtual environment using the hybrid dataset showed a slight decrease in all metrics compared to using the virtual dataset. For example, the mean F1-score for the three postures was 0.97 when using the virtual dataset and 0.90 when using the hybrid dataset. This can be attributed to the gap in the appearance domain caused by the difference between the natural and synthetic image formation processes. A real image is the product of an image acquisition process that inherently captures a variety of different natural phenomena, such as motion noise and material properties. On the other hand, a synthetic image is the result of a light transport simulation and rendering [18]. * The real environment recognition results using the hybrid dataset showed impressive improvements in all metrics compared to the virtual dataset.