# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 2 Literature Review  
The use of YOLO is in line with recent research highlighting the effectiveness of certain models in posture detection, underlining their relevance and applicability in healthcare for the elderly [1; 6; 32]. The first is an automated image-based fall detection system that can be used to monitor geriatric behaviour using the YOLOv3 algorithm [1], where fall events are identified and alerted as soon as they are detected. To meet the requirements of low cost, high accuracy, and real-time computation, the YOLOv3 object detection algorithm was combined with the IFADS fall detection method [33]. In addition, an improved YOLOv5s algorithm was recently proposed [6]. The work aims to improve the efficiency and accuracy of lightweight fall detection. Another fall detection system used the YOLOv8 algorithm [32]. The YOLOv8 algorithm helps detect whether a single person captured by surveillance cameras falls within an acceptable time frame.  
Other advanced deep learning models have been used in elderly healthcare applications. For example, some works have focused on developing methods for elderly healthcare to identify human activity and achieve fall detection [34; 35; 36; 37]. First, a mobile-enabled fall detection framework was presented in [34]. The framework is based on a hybrid deep learning model that combines a convolutional neural network (CNN) to extract the local representative features from the accelerometer sensor embedded in smartphones with a long short-term memory (LSTM) network to learn the dependencies between the features inferred from the sensor data. In addition, a fall detection method for elderly people was presented in [35]. It is based on an AlexNet CNN to produce a wearable IoT system that can detect falls. Data are collected from sensor devices placed on the subject's body in six different positions for feature extraction and sensor data analysis. Multi-linear principal component analysis is then applied to reduce the dimensionality of the derived features. Another fall detection method for elderly people was developed using a three-dimensional CNN as a suitable classifier for motion detection with a low-resolution infrared array sensor [36]. An LSTM network with feature extraction was also implemented for comparison. Finally, a radar-based fall detection method for elderly people was described, which is based on time-frequency analysis by applying the short-time Fourier transform to each radar return signal [37]. The resulting spectrograms are converted into binary images, which are fed into the CNN for high-level feature learning.  
Other applications using deep learning models are related to health assistance in daily life [38; 39; 40]. For example, a system based on deep neural networks for identifying the activity of elderly residents in care facilities was presented in [38]. Another paper focused on the identification and prediction of anomalous behaviour by studying different deep neural network methodologies, including CNN-LSTM and Autoencoder CNN-LSTM [39]. Finally, a methodology based on a deep neural network, a two-layer framework, and marker-based stigmergy for identifying the activities of lonely older people was described in [40].