Human Activity Recognition using Smartphone Sensor Dataset
V Arun1, Hariharan T.A2, K.V Srihari3, M. Abhijeet Sriram4
1V. Arun, Assistant Professor, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
2Hariharan T.A, UG Scholars SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
3K.V Srihari, UG Scholars SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
4M. Abhijeeth Sriram, UG Scholars SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1443-1449 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6232048419/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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 is an intensive space of a machine learning analysis owing to its applications in health care, smart environments, Homeland Security, Research, etc. Study for human action recognition observes that researchers have an interest largely within the daily activities of the human. The identification process can be done by manipulating the information obtained from the surrounding environment or from sensors attached to human body. This paper introduces a deliberate act investigation of movement sensor conduct for human action acknowledgment by means of cell phones. Tangible data arrangements are gathered by means of cell phones once members perform run of the mill and every day human exercises. Exploratory outcomes on a freely accessible data-set demonstrate that combination of both accelerometer and gyroscope information adds to get preferred acknowledgment execution over that of utilizing single source information.
Keywords: Human Activity Recognition (HAR), Machine Learning, Data Science, Smartphone and Wearable Sensors.
Scope of the Article: Machine Learning