A hybrid posture detection framework: Integrating machine learning and deep neural networks

Liaqat, S., Dashtipour, K., Arshad, K., Assaleh, K. and Ramzan, N. (2021) A hybrid posture detection framework: Integrating machine learning and deep neural networks. IEEE Sensors Journal, 21(7), pp. 9515-9522. (doi: 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%.

• Item Type: Articles
• Additional Information: This work is supported in part by the Ajman University Internal Research Grant.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Dashtipour, Dr Kia
• Authors: Liaqat, S., Dashtipour, K., Arshad, K., Assaleh, K., and Ramzan, N.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: IEEE Sensors Journal
• Publisher: IEEE
• ISSN: 1530-437X
• ISSN (Online): 1558-1748
• Published Online: 01 February 2021
• Copyright Holders: Copyright © 2021 IEEE
• First Published: First published in IEEE Sensors Journal 21(7): 9515-9522
• Publisher Policy: Reproduced in accordance with the publisher copyright policy