Human Activity Recognition using Smartphone
Radha Mothukuri1, Tunuguntla Aishwarya2, Chalasani Himasree3, Dasari Pushkar Babu4
1Radha Mothukuri, Assistant Professor, Dept. of CSE, Koneru Lakshmaih Education Foundation, Vaddeswaram, Guntur-522502, A.P, India.
2Tunuguntla Aishwarya, Dept. of CSE,Koneru Lakshmaih Education Foundation, Vaddeswaram, Guntur, A.P, India
3Chalasani Himasree, Dept. of CSE, Koneru Lakshmaih Education Foundation, Vaddeswaram, Guntur, A.P, India
4Dasari Pushkar Babu, Dept. of CSE, Koneru Lakshmaih Education Foundation, Vaddeswaram, Guntur, A.P, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 10159-10163 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4521118419/2019©BEIESP | DOI: 10.35940/ijrte.D4521.118419
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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: This paper consists of all the actions that are done by the people by using mobile phone. We used to record the actions by using the mobile phone. The main aim of this paper is to construct a classification model that is required for identifying actions of the people. This paper is used mainly to get solution of multi-classification problems. By using in a theoretic way, we can understand what the problem is, but we can only solve the problem by performing it in mathematical way. We can get very perfect result by solving in the mathematical way. Here we are deriving the actions of the persons using mobile phone, in a phone there are many sensors present in it. The sensors used in this paper are Accelerometer, Gyroscope. They are required for determining the actions of the person. The output obtained in this paper is used to compare the values in the term’s accuracy and precision. It uses a 3-dimension based accelerometer in order to collect the values obtained; there we determined that 31 values we contained in it. All the actions that are present are derived by using machine learning algorithms, they are, Naïve Bayes Classifiers, support vector machine, and neural networks. The output of the action determination by using the dataset required is used to determine a decrease of marking work to accomplish similar execution with machine learning.
Keywords: Machine Learning, SVM, Neural networks.
Scope of the Article: Machine Learning.