# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 4 Machine Learning in Posture Recognition
Research such as [61; 74; 95; 96; 136; 137; 138] applied rule-based algorithms such as decision tree, random forest, and k-nearest. Rule-based algorithms are successful regarding low data dependency, training and testing time, no black box issues, and reliability. On the other hand, they have constraints on complex or non-linear relationships in data. Fuzzy-based position classification is applied by [110] and [62]. Fuzzy logic can increase the success ratio in uncertainty between positions. The former had an auspicious result of 99% accuracy, while the latter, using the fuzzy logic-based system, has more than 80% accuracy, which is less than most of the neural network and rule-based algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **ML Algorithm** & **Accuracy** \\ \hline
[116] & Very Deep Convolutional Networks & 98.3\% \\ \hline
[95] & Random forest & 98.7\% \\  & Decision tree & 97.1\% \\  & K-nearest neighbors & 94.3\% \\ \hline
[139] & Graph convolutional network & 92.7\% \\ \hline
[118] & Light Convolution Core (LCC) Convolutional & 99.5\% \\  & Block Attention Module (CBAM) & \\ \hline
[119] & Recurrent neural network & 88.0\% \\ \hline
[120] & Small scale convolutional neural network & 91.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Machine learning algorithms in sitting posture recognition. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **ML Algorithm** & **Accuracy** \\ \hline \multirow{6}{*}{[121]} & \(\mu\)LBP + Logistic Regression & 62.4\% \\  & \(\mu\)LBP + decision tree & 81.7\% \\  & \(\mu\)LBP + naive Bayes classifier & 61.6\% \\  & \(\mu\)LBP + neural network & 87.3\% \\  & \(\mu\)LBP + random forest & 92.6\% \\ \hline \multirow{6}{*}{[87]} & DCNN using 2 sensors & 73.4\% \\  & One branch TSPN using 2 sensors & 86.6\% \\  & Four Unimodal Models using 2 sensors & 76.7\% \\  & TSPN using 2 sensors & 90.6\% \\  & TSPN using 2 sensors (no data augmentation) & \\ \hline \multirow{6}{*}{[96]} & K-nearest neighbors & 97.91\% \\  & Support Vector Machine & 98.56\% \\  & Decision tree & 98.71\% \\  & Random forest & 99.1\% \\  & Naive Bayes classifier & 89.53\% \\  & Boosting & 99.01\% \\  & K-means (unsupervised) & 57.79\% \\ \hline \multirow{6}{*}{[136]} & Random forest & 96.6\% \\  & Logistic & 95.5\% \\  & Decision tree & 94.3\% \\ \hline \multirow{6}{*}{[74]} & K-nearest neighbors & 95.0\% \\  & Support vector machines & 85.35\% \\  & K-nearest neighbors & 90.3\% \\  & Logistic Regression & 79.61\% \\  & Decision tree & 88.26\% \\  & LightGBM & 95.41\% \\ \hline \multirow{6}{*}{[137]} & k-NN & 98.07\% \\  & Random forest & 98.65\% \\  & SVM (linear) & 95.96\% \\  & Decision tree & 98.85\% \\  & LightGBM & 99.03\% \\ \hline \multirow{2}{*}{[62]} & Rule-based fuzzy logic & \textgreater{}80\% \\ \hline \multirow{2}{*}{[110]} & Fuzzy logic & 99.0\% \\ \hline \multirow{2}{*}{[90]} & ISOM-based sitting posture recognition algorithm & 95.7\% \\ \hline \multirow{2}{*}{[111]} & Deep recurrent hierarchical network & 91.5\% \\ \hline \multirow{2}{*}{[101]} & naive Bayes & 91.98\% \\  & SVM & 99.51\% \\  & KNN & 99.7\% \\ \hline \multirow{2}{*}{[122]} & Linear SVM, Poly-SVM, RBF SVM & 98.6\% \\ \hline \multirow{2}{*}{[131]} & Faster-R-CNN & 76.76\% \\ \hline \multirow{6}{*}{[85]} & Gradient Descent & 96.63\% \\  & Gradient Descent with Momentum & 97.67\% \\ \cline{1-1}  & Gradient Descent with Adaptive Learning Rate & 97.79\% \\ \cline{1-1}  & Resilient Backpropagation & 98.29\% \\ \cline{1-1}  & Levenberg-Marquardt & 98.76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._
Table 8 provides a summary of the machine learning algorithm used in sensor-based posture recognition approaches. It can be seen that RF, DT, KNN, and SVM are mostly widely used algorithms, though they have been replaced by deep learning algorithms recently. Several machine learning algorithms are applied to video and image-based data for sitting posture classifications. Table 9 shows the methodology, data volume, and accuracy of these methods.