# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Image Sensors
#### 3.6.2 Kinect Camera-Based Technologies  
General video cameras are more versatile but may lack the specialized features necessary for detailed body tracking and analysis. The Kinect camera is a depth-sensing camera system developed by Microsoft primarily for gaming and interactive entertainment applications. These cameras excel in depth sensing, body tracking, and real-time feedback, making them ideal for applications requiring accurate posture recognition and motion analysis. In-depth sensing allows for accurate 3D reconstruction of the scene, in contrast to general video cameras, which typically capture 2D images without depth information. Kinect cameras typically have a wide field of view, allowing them to acquire the complete body or multiple people within a scene. Kinect can be used in office environments to monitor employees' sitting posture throughout the workday [129], as shown in Figure 10.  
[PERSON] [130] used joint information provided by the Kinect sensor to detect neck and torso angles. By tracking these angles from depth images over time, the author devised a method that determines whether a sitting posture is healthy or not with 100% accuracy. [PERSON] proposed a sitting posture monitoring system using Kinect, which employs an IR depth camera to  
Figure 10: Example of a Kinect sensor-based system for sitting posture recognition [129]. record the user's sitting posture data and alert them to incorrect postures. [PERSON] et al. [131] used a Microsoft Kinect sensor to detect and monitor key bone points of the human body. Faster R-CNN is employed for scene recognition to reliably identify objects and extract relevant information. Gaussian-mixture behavioral clustering is then used for semantic analysis to comprehend the scene. By combining the pertinent aspects of the scene with the human skeleton's features, the system generates semantic features to distinguish between different sitting postures. He [124] obtained the three-dimensional position information of joints using Kinect's real-time skeleton tracking technology to monitor sitting posture and alert the user if bad posture is detected. [PERSON] [132] utilized the Kinect sensor to detect sitting positions, which were subsequently utilized to train posture recognition models such as Support Vector Machine (SVM) and artificial neural network (ANN). MATLAB was employed for training the posture recognition algorithms. The experimental results and analysis indicated that SVM with a linear kernel outperformed other models in terms of accuracy. [PERSON] [133] suggested a sitting posture recognition system for students in classrooms using a deep learning neural network model by extracting sitting properties. A deep fusion neural network method is suggested by [PERSON] [116] based on Visual Geometry Group (VGG), Resnet, and deep transformer networks. Images are captured using Kinect 2.0, which is further improved through random flipping and random. The VGG network extracts shallow features, the Resnet adds constant mapping to extract multi-dimensional features from the image, and the Vision Transformer network uses this fused feature information to recognize six different sitting postures with an accuracy of 98.3%. [PERSON] [134] developed a database of sitting postures using Kinect to collect body index maps. They employed the Structural Similarity Index (SSIM) to monitor posture changes and the Broad Learning System (BLS) to categorize postures in real-time. Additionally, they proposed a double-threshold cascade algorithm to detect exaggerated frames in videos. Experimental results showed that their algorithm could identify eight different sitting postures at 162 frames per second with an accuracy of 98.58%. [PERSON] [126] captured real-time video data from pupils who are seated using Kinect cameras and identified two sitting postures using CNN and claimed that the CNN-based feature learning approach offers strong anti-interference, good accuracy, and resistance to light and environmental influences.