A Hybrid Posture Detection Framework: Integrating Machine Learning and Deep Neural Networks

Abstract

The posture detection received lots of attention in the fields of human sensing and artificial intelligence. Posture detection can be used
for the monitoring health status of elderly remotely by identifying their postures such as standing, sitting and walking. Most of the current studies
used traditional machine learning classifiers to identify the posture. However, these methods do not perform well to detect the postures accurately.
Therefore, in this study, we proposed a novel hybrid approach based on machine learning classifiers (i. e., support vector machine (SVM), logistic
regression (KNN), decision tree, Naive Bayes, random forest, Linear discrete analysis and Quadratic discrete analysis) and deep learning
classifiers (i. e., 1D- convolutional neural network (1D-CNN), 2D-convolutional neural network (2D-CNN), LSTM and bidirectional LSTM) to
identify posture detection. The proposed hybrid approach uses prediction of machine learning (ML) and deep learning (DL) to improve the
performance of ML and DL algorithms. The experimental results on widely benchmark dataset are shown and results achieved an accuracy of
more than 98%.