Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/32241
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A hybrid posture detection framework: Integrating machine learning and deep neural networks
Author(s): Liaqat, Sidrah
Dashtipour, Kia
Arshad, Kamran
Assaleh, Khaled
Ramzan, Naeem
Contact Email: kia.dashtipour@glasgow.ac.uk
Keywords: Posture detection
Hybrid Approach
Deep Learning
Machine Learning
Issue Date: 1-Feb-2021
Date Deposited: 4-Feb-2021
Citation: Liaqat S, Dashtipour K, Arshad K, Assaleh K & Ramzan N (2021) A hybrid posture detection framework: Integrating machine learning and deep neural networks. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2021.3055898
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%.
DOI Link: 10.1109/jsen.2021.3055898
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Notes: Output Status: Forthcoming

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