TY - GEN
T1 - Comparing the Performance of Different Classifiers for Posture Detection
AU - Suresh Kumar, Sagar
AU - Dashtipour, Kia
AU - Gogate, Mandar
AU - Ahmad, Jawad
AU - Assaleh, Khaled
AU - Arshad, Kamran
AU - Imran, Muhammad Ali
AU - Abbasi, Qammer
AU - Ahmad, Wasim
N1 - Publisher Copyright:
© 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2022
Y1 - 2022
N2 - 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%.
AB - 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%.
KW - Deep learning
KW - Detecting Alzheimer
KW - Machine learning
UR - https://www.scopus.com/pages/publications/85125240581
U2 - 10.1007/978-3-030-95593-9_17
DO - 10.1007/978-3-030-95593-9_17
M3 - Conference contribution
AN - SCOPUS:85125240581
SN - 9783030955922
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 210
EP - 218
BT - Body Area Networks. Smart IoT and Big Data for Intelligent Health Management - 16th EAI International Conference, BODYNETS 2021, Proceedings
A2 - Ur Rehman, Masood
A2 - Zoha, Ahmed
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th EAI International Conference on Body Area Networks, BODYNETS 2021
Y2 - 25 December 2021 through 26 December 2021
ER -