An Electric Eye for Human Activity Recognition: A Hybrid Neural Network
K. N. Apinaya Prethi1, M. Sangeetha2, S. Nithya3, G. Priyadharshini4, N. Anithadevi5
1K. N. Apinaya Prethi*, Assistant Professor , Department of Computer Science & Engineering at Coimbatore Institute of Technology, India.
2Dr. M. Sangeetha, Associate Professor, Coimbatore Institute of Technology, Coimbatore, India.
3Ms. Nithya, Assistant Professor, Department of Computer Science & Engineering at Coimbatore Institute of Technology, India.
4Ms. G. Priyadharshini, Assistant Professor, Department of Computer Science and Engineering at Coimbatore, India.
5Dr. N. Anithadevi, Assistant Professor, Department of Computer Science and Engineering Information Technology, Coimbatore Institute of Technology, Coimbatore, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2806-2809 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5957029320 /2020©BEIESP | DOI: 10.35940/ijeat.C5957.029320
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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: A real time detection of human movements is a practical solution to monitor aged people or mentally challenged people with the permission of their family. Household person is needed to monitor the elder and differently abled people. Instead of monitoring their activities with the help of other people, smart phones are used as a remote to monitor their activities and simultaneously send the message to their family members. The accelerometer sensor placed in the mobile phones. It is used to identify the activities of the person who holds the mobile phones. The most commonly used classifier technique is Naive Bayes classifier which has a limitation of handle with the large set of data. To overcome this defect, the proposed system classifies the data using k-nearest neighbor (K-NN) technique and Neuroevolution. This system recognize some representative human movements such as walking, climbing upstairs, climbing downstairs, standing, sitting and running ,using a conventional mobile equipped with a single tri-axial accelerometer sensor.
Keywords: K-NN, Naive Bayes classifier, Neuroevolution, tri-axial accelerometer sensor