Machine Learning Vs Deep Learning: Which Is Better For Human Activity Recognition
Nasim Uddin1, Mayank Pratap Singh2, M. Vaidhehi Rakesh3
1Nasim Uddin*, Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai, India
2Mayank Pratap Singh, Department of Computer Science and Engineerin, SRM Institute of Science and Technology Chennai,India.
3Mrs. M. Vaidhehi Rakesh, Department of Computer Science and Engineering SRM Institute of Science and TechnologyChennai, India
Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1344-1349 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6310029320/2020©BEIESP | DOI: 10.35940/ijeat.C6310.049420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human activity recognition(HAR) is used to describe basic activities that humans are performing using the sensors that we have in smartphones. The data for this activity recognition is captured by various sensors of mobile phones or wristbands such as accelerometer, gyroscope and gravity sensors.HAR has grabbed the attention of various researchers due to its vast demand in the fields of sport training, security, entertainment health monitoring,computer vision and robotics. In this project we compare different machine learning and deep learning algorithms to find a better approach for HAR. The dataset comprises six activities i.e. walking, sleeping, sitting,moving upward, moving downwards and standing.In this demonstration we also showed confusion matrix,accuracy and multi log loss of various algorithms. With the help of accuracy, confusion matrix of algorithms we compare and determine the best approach for HAR. This will help in future research to map the activities of humans using one of the best approaches used.
Keywords: Human activity recognition, machine learning, deep learning