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Comparing the Performance of Different Classifiers for Posture Detection

Suresh Kumar, Sagar; Dashtipour, Kia; Gogate, Mandar; Ahmad, Jawad; Assaleh, Khaled; Arshad, Kamran; Imran, Muhammad Ali; Abbasi, Qammer; Ahmad, Wasim


Sagar Suresh Kumar

Khaled Assaleh

Kamran Arshad

Muhammad Ali Imran

Qammer Abbasi

Wasim Ahmad


Human Posture Classification (HPC) is used in many fields such as human computer interfacing, security surveillance, rehabilitation, remote monitoring, and so on. This paper compares the performance of different classifiers in the detection of 3 postures, sitting, standing, and lying down, which was recorded using Microsoft Kinect cameras. The Machine Learning classifiers used included the Support Vector Classifier, Naive Bayes, Logistic Regression, K-Nearest Neighbours, and Random Forests. The Deep Learning ones included the standard Multi-Layer Perceptron, Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTM). It was observed that Deep Learning methods outperformed the former and that the one-dimensional CNN performed the best with an accuracy of 93.45%.

Presentation Conference Type Conference Paper (Published)
Conference Name 16th EAI International Conference, BODYNETS 2021
Start Date Oct 25, 2021
End Date Oct 26, 2021
Acceptance Date Aug 31, 2021
Online Publication Date Feb 11, 2022
Publication Date 2022
Deposit Date Apr 26, 2022
Publisher Springer
Pages 210-218
Series Title Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Series Number 420
Series ISSN 1867-822X
Book Title Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021
ISBN 978-3-030-95592-2
Keywords Machine learning, Deep learning, Detecting Alzheimer
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