Sensor based Human Activity Recognition
Nivetha Vasanthi G1, Rohini R2, Shenbagavalli V3, Gomathi V4
1G. Nivetha Vasanthi*, Bachelor of Engineering, Computer Science and Engineering, National Engineering College, Tamil Nadu, India.
2R. Rohini, Bachelor of Engineering, Computer Science and Engineering ,National Engineering College, Tamil Nadu, India.
3V.Shenbagavalli, Bachelor of Engineering, Computer Science and Engineering, National Engineering College, Tamil Nadu, India.
4Dr. V.Gomathi, Professor & Head of the Department, Computer Science and Engineering, National Engineering College, kovilpatti, Tamil Nadu, India.
Manuscript received on April 20, 2020. | Revised Manuscript received on May 01, 2020. | Manuscript published on May 10, 2020. | PP: 1008-1012 | Volume-9 Issue-7, May 2020. | Retrieval Number: F4324049620/2020©BEIESP | DOI: 10.35940/ijitee.F4324.059720
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© 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 to recognize another person’s activities and it is one of the active research areas in the computer field. The goal of this System is to understand people’s actions and interactions. We proposed a method of Human Activity is by predicting the person’s activity, their personality, and their psychological state like Human activity recognition (HAR). We propose a recurrent neural network of deep learning architecture. The critical factor of RNN includes bidirectional connection that is simply called from the input node, the information only flows in forwarding direction after that it passthrough so many hidden layers to reach the output.. This system is to design the six different activities of a human. The final model should use as a good source of information about human’s daily activities. The dataset has taken from UCI Machine Learning Repository. Our system accuracy is higher than the previous results.
Keywords: Long short-term memory, RNN, Activity.
Scope of the Article: Adhoc and Sensor Networks