## 2 Related Works
### Pose Classification
\begin{table}
\begin{tabular}{c c c c} \hline
**Features** & **\# of Samples** & **Key Discovery** & **Recommendations** \\ \hline
7 feature points & 60 subjects (30 male and 30 female) & \
\begin{tabular}{c} The use of head and shoulder \\ posture (Chin, Manubrium, left and \\ right shoulder). The use of physical \\ markers placed on this keypoints. \\ \end{tabular} &
\begin{tabular}{c} Consideration of Deep Learning \\ and CNN [48] \\ \end{tabular} \\ \hline
18 key points and 10 features & 146 random images & \
\begin{tabular}{c} The use of lower extremities \\ points (angles) \\ \end{tabular} &
\begin{tabular}{c} It was also observed that in \\ images where lower body was not \\ completely visible due to \\ occlusion (i.e., hip joints were not \\ detected, algorithm could not \\ correctly classify between sitting \\ and standing postures) [49] \\ \end{tabular} \\ \hline
6 key points and 10 features & 11 subjects (nine males and two females) & \
\begin{tabular}{c} 2 Kinect cameras (front and lateral), \\ RULA- risk assessment (manually \\ measured) and variables, such as \\ upper-arm abduction and neck side \\ bending, are measured more easily \\ and accurately from front-view, and \\ other variables, such as trunk \\ flexion and neck flexion, can be \\ measured more accurately from \\ side- view than from the front-view. \\ \end{tabular} &
\begin{tabular}{c} 2 Kinect cameras (front and lateral), \\ RULA- risk assessment (manually \\ measured) and variables, such as \\ upper-arm abduction and neck side \\ bending, are measured more easily \\ and accurately from front-view, and \\ other variables, such as trunk \\ in video [50] \\ \end{tabular} \\ \hline
3 points in the spine, 1 neck and 1 hip & 6 subjects & \
\begin{tabular}{c} Combination of IMU sensors and \\ Kinect Cameras \\ \end{tabular} &
\begin{tabular}{c} Consideration of more \\ areas/points with respect to \\ their position [51] \\ \end{tabular} \\ \hline
10 feature points & \
\begin{tabular}{c} A pilot study utilizing LIDAR \\ sensors to capture 3D images then \\ convert into 2D with the use of \\ Convolutional Neural Network \\ \end{tabular} &
\begin{tabular}{c} Capturing the 3D point cloud and \\ transformation is computationally \\ expensive. Therefore, this needs \\ to be transformed into a \\ small-scale neural network \\ running in less computational \\ expensive devices [52]. \\ \end{tabular} \\ \hline
10 feature points & \
\begin{tabular}{c} Converting 2D into 3D images and \\ applying Posenet.js and ResNet50 \\ model for the detection of \\ these points. \\ \end{tabular} &
\begin{tabular}{c} A small-scale model that can run \\ an efficient and accurate model \\ detecting more feature points is \\ recommended [53]. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Key Insights from the Early Studies (2017–2022). As seen in the Table 2 below, there are key points that can be measured with either sensors directly attached to human body or vision-based (camera-based) sensors. It is noticeable that in the literature, the three points in the spine, only being measured using direct-based measurement (accelerometers). Therefore, some of the features mentioned in this table will be use in the proposed study. Additionally, some features that were captured using direct-based measurements will be measured using camera-based sensors. The succeeding sections shows the important concepts and techniques to achieve an acceptable monitoring system for proper and improper sitting posture. The study is divided into two major tasks. First, to develop an acceptable dataset with the guidance and supervision of the Domain Experts (Licensed Physical Therapists). This is essential since this study has added distinct key points such as chin, and three key points in the spine (thoracic, thoraco-lumbar and lumbar). This includes the use of sufficiently fast Human Pose Estimation, and acceptable algorithm for pose classification. To locate raw feature points using human pose estimation, this study has used existing framework.