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

EasyChair Preprint 1495

Abstract

Nowadays analyze of human activity and human behavior can be useful for software design especially for patients. So, human activity recognition is important. The aim of this research was find the best algorithm for human activity recognition. We used Logistic Regression, SVM with RBF kernel; CNN, LSTM, Bi-Directional LSTM and CNN-LSTM algorithms for analyze the data. In the data analyze the accuracy and training time measured and compared. The most accuracy belonged to the CNN-LSTM and Bi-Directional LSTM and the least training time belonged to the SVM with RBF kernel.

Keyphrases: Bi-directional LSTM, CNN, CNN-LSTM, Human Activity Recognition, LSTM, SVM with RBF kernel, logistic regression

@booklet{EasyChair:1495,
  author    = {Babak Moradi and Mohammad Aghapour and Afshin Shirbandi},
  title     = {Compare of  Machine Learning And Deep Learning Approaches for Human Activity Recognition},
  howpublished = {EasyChair Preprint 1495},
  year      = {EasyChair, 2019}}