# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 4 Machine Learning in Posture Recognition  
In posture identification, machine learning algorithms are used for classification, prediction, and clustering. ML algorithms are very useful in any area, from agriculture, finance, and engineering to space research. We can classify algorithms into two parts: one is the probabilistic-based methods, and the other is the ruling-based methods. Both are widely used in sitting posture recognition research to identify positions. Deep learning-based sitting posture recognition algorithms have been developed, achieving high accuracy and addressing privacy concerns summarized in Table 7. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ref.** & **Author** & **\# of Postures** & **ML Algorithm** &
\begin{tabular}{l} **Overall** \\ **Accuracy** \\ \end{tabular} \\ \hline
[130] & [PERSON] 2017 & 5 & Deep Learning & 100\% \\
[131] & [PERSON] 2018 & 9 & Faster R-CNN & 95.60\% \\
[124] & [PERSON] 2018 & 4 & ANN & \textgreater{}90\% \\
[132] & [PERSON] (2019) & 2 & SVM, ANN & 97.10\% \\
[133] & [PERSON] 2019 & 2 & Keras Deep Learning & 90\% \\
[126] & [PERSON] 2021 & 2 & CNN & 82.86\% \\
[111] & [PERSON] 2021 & 5 & MobileNetV2 & 87.60\% \\
[116] & [PERSON] 2023 & 6 & Deep Fusion Neural Network & 98.30\% \\
[119] & [PERSON] 2003 & 8 & RNN (2 BLSTM and 3 BGRU) & 88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Kinect camera-based solutions. [PERSON] [103] proposed a faster R-CNN algorithm in feature extraction, and then the Gaussian Mixture behavioral algorithm was used for scene understanding. This method is very powerful against multi-object detection. The accuracy of this work is 84.48%. [PERSON] [104] trained sensor data with a three-layer backpropagating neural network algorithm to identify normal sitting, slight hunchback, and severe hunchback positions. The neural network-based method is very effective and robust; accuracy is 98.75%. [PERSON] [134] developed a new identification method using a broad learning system, which is a different type of deep learning algorithm. The proposed technique makes the process simple and faster than other neural networks. In this work, accuracy is 98.58% with 162 fps speed. [PERSON] [135] proposed the spiking neural network method, which is the combination of logistic regression and liquid state machine. Data collected from 19 participants are classified into 15 classes, such as upright, leaning right, leaning back, and sitting on the leading edge. [PERSON] [89] applied the feedforward neural network algorithm to classify nine sitting postures. In this work, data is collected in real-time, and 90.64% accuracy is obtained. Neural networks and deep learning classification algorithms are very successful in posture recognition. One of the reasons is the size of the data, and the other one is that NN algorithms can be easily adapted to different types of data in the training process. [PERSON] [101] used inertial sensor data to generate five sitting chair positions. The naive Bayes, Support Vector Machine, and k-nearest network algorithms are applied in the training process. SVM gives the highest accuracy with 99.9% after correlation-based feature selection. In this work, there are five simple sitting positions classified as left movement, right movement, front movement, back movement, and straight movement. Posture recognition works have some constraints, such as limited visibility and occlusion between objects. [PERSON] [111] proposed a deep recurrent hierarchical network algorithm to reduce this common problem. This algorithm uses an RGB depth frame and produces high-quality features from these frames. The accuracy of this novel algorithm is 91.47% with 10 fps data. Neural networks are frequently used for sitting posture recognition [67; 116; 119; 125; 128]. Proposed methods use neural networks and deep learning in the feature extraction and classification part of the recognition. One of the constraints of using these methods is the training time and overfitting. Success ratios in most of the work are more than 90%.