# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Image Sensors
#### 3.6.1 Video Camera-Based Technologies  
In posture recognition, both standard RGB and depth cameras are frequently used. RGB cameras are more affordable and easier to use. Depth cameras measure the distance between objects, and it increases the accuracy of the recognition. Other types of cameras are single-camera, multi-camera, static and dynamic cameras, wearable cameras, and infrared cameras, which are also used in sitting posture recognition. Multi-cameras and dynamic cameras capture more frames, and it increases the robustness of the recognition and processing time. Single cameras monitor only from one angle, and it might be difficult to determine posture. Data is collected from video cameras, and several computer vision methods are applied to identify sitting positions. Infrared cameras are also used in posture recognition, especially in harsh environments such as nights and in non-visible weather conditions. Computer vision-based methods are very frequently used in face recognition, object identification, and scenario recognition. Indoor, outdoor, offline, real-time, static, and moving cameras also affect the success of these methods. Data collection, background subtraction, object detection, object tracking, feature extraction, and classification are the steps of the regular computer vision algorithm in posture identification. [PERSON] et al. [110] proposed a computer vision-based home monitoring system for elderly people. Change detection and object tracking methods are applied to find out the features of moving objects. The fuzzy logic-based classification algorithm gives 74.27% accuracy for four class problems. The proposed algorithm is successful for home monitoring emergency applications. [PERSON] [111] proposed a hierarchical image composition method in video data for eight-class posture identification. RGB-D frames are retrieved from video sequences, and then the frame semantic method is applied to finalize layers. Hierarchical ANN algorithms get RGB-D frames as an input and output a prediction tree. The proposed algorithm has 91.47% accuracy for video applications. This method is reliable even though 30% of the object's torso is visible in the camera. Infrared and video camera solutions use the following procedure to identify sitting postures. * Data is collected from video cameras. The camera might be static, moving, or multiple. Different sets of sitting postures should be collected, and labels prepared to correct and incorrect classes. * Frames are extracted, and image and data preprocessing techniques are used to enhance the quality of input data. * Images can be used as color images or converted to RGB format. * The pose estimation model is used to detect key points in the human body, such as joints and limbs. * Object detection and tracking methods are applied to extract feature sets. * Train a machine learning model to recognize different postures based on the extracted features, such as deep learning, rule-based algorithms, and the Bayes networks. Techniques for real-time applications and feedback alert systems can be integrated into the model. Ref. [112] worked on exercise pose recognition from RGB video data. Model formulated using 2D coordinates from 2D exercise pose. There are 18 features extracted from human body joints. 97.01% accuracy is achieved with several sets of data sets. Sitting posture recognition has some limitations because of camera position, weather conditions, and obstacles. Infrared cameras solve some of the limitations, especially those related to weather conditions. [PERSON] proposed human detection and posture estimation algorithms from infrared camera inputs. The k-means clustering algorithm is applied to the IR frame, and then morphological filtering is applied to detect objects. In pose estimation, template matching, and threshold techniques are used. [PERSON] used a video camera, infrared camera, and depth sensor to collect driver posture data.