Classification of Activities of Daily living using Smart Phones
R.Ranjith1, Anju.S.Pillai2
1R.Ranjith, Department of Electrical and Electronics Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
2Dr.Anju.S.Pillai, Department of Electrical and Electronics Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
Manuscript received on 5 August 2019. | Revised Manuscript received on 9 August 2019. | Manuscript published on 30 September 2019. | PP: 2984-2988 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4822098319/2019©BEIESP | DOI: 10.35940/ijrte.C4822.098319
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: Smart phones have become an integral part of everyday human life. These phones are packed with various sensors for different purposes. Most of them are used for understanding the environment in which the user uses the phone so that the device could respond rapidly. Indirectly the phone extracts context information of the users like the activity performed using accelerometer and gyroscope sensors. This information can be used for a variety of applications like home automation, smart environment, etc to perform automatic changes to the environment without direct input from the user. This paper deals with the classification of activities of daily living like walking, jogging, sitting, standing, upstairs and downstairs using the data collected from accelerometer sensor within the smart phone. A comparative analysis has been performed on different machine learning techniques for activity classification.
Keywords: Activities of Daily Living Classification, Smart Phone, Sensors, Accelerometer, Multilayer Perceptron, Support Vector Machine and Decision Tree.
Scope of the Article: Classification.