Please use this identifier to cite or link to this item:
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:
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.
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
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Notes: Output Status: Forthcoming

Files in This Item:
File Description SizeFormat 
09343347.pdfFulltext - Published Version248.18 kBAdobe PDFUnder Permanent Embargo    Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.

This item is protected by original copyright

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.