Research Article
Comparing the Performance of Different Classifiers for Posture Detection
@INPROCEEDINGS{10.1007/978-3-030-95593-9_17, author={Sagar Suresh Kumar and Kia Dashtipour and Mandar Gogate and Jawad Ahmad and Khaled Assaleh and Kamran Arshad and Muhammad Ali Imran and Qammer Abbasi and Wasim Ahmad}, title={Comparing the Performance of Different Classifiers for Posture Detection}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings}, proceedings_a={BODYNETS}, year={2022}, month={2}, keywords={Machine learning Deep learning Detecting Alzheimer}, doi={10.1007/978-3-030-95593-9_17} }- Sagar Suresh Kumar
Kia Dashtipour
Mandar Gogate
Jawad Ahmad
Khaled Assaleh
Kamran Arshad
Muhammad Ali Imran
Qammer Abbasi
Wasim Ahmad
Year: 2022
Comparing the Performance of Different Classifiers for Posture Detection
BODYNETS
Springer
DOI: 10.1007/978-3-030-95593-9_17
Abstract
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%.
