Compare of Machine Learning and Deep Learning Approaches for Human Activity Recognition

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. This research aimed to find the best algorithm for human activity recognition. We used Logistic Regression, SVM with RBF kernel; CNN, LSTM...

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Bibliographic Details
Published in2022 30th International Conference on Electrical Engineering (ICEE) pp. 592 - 596
Main Authors Moradi, Babak, Aghapour, Mohammad, Shirbandi, Afshin
Format Conference Proceeding
LanguageEnglish
Published IEEE 17.05.2022
Subjects
Activity recognition
Bi-Directional LSTM
Bidirectional control
CNN
CNN-LSTM
Electrical engineering
Human activity recognition
Human computer interaction
Logistic Regression
LSTM
Machine learning algorithms
Support vector machines
SVM with RBF kernel
Training
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Summary:Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. This research aimed to find the best algorithm for human activity recognition. We used Logistic Regression, SVM with RBF kernel; CNN, LSTM, Bi-Directional LSTM, and CNNLSTM algorithms for analyzing the data. The data analysis measured and compared the accuracy and training time. The most accuracy belonged to the CNN-LSTM and Bi-Directional LSTM, and the least training time belonged to the SVM with RBF kernel
ISSN:2642-9527
DOI:10.1109/ICEE55646.2022.9827335