Deep Learning for Human Action Recognition with Convolution Neural Network

Authors

  • S. Karthickkumar  Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India
  • Dr. K. Kumar  Computer Science and Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India

DOI:

https://doi.org/10.32628/CSEIT206466

Keywords:

Human activity recognition, Convolutional neural network, 3D accelerometer data.

Abstract

In recent years, deep learning for human action recognition is one of the most popular researches. It has a variety of applications such as surveillance, health care, and consumer behavior analysis, robotics. In this paper to propose a Two-Dimensional (2D) Convolutional Neural Network for recognizing Human Activities. Here the WISDM dataset is used to tarin and test the data. It can have the Activities like sitting, standing and downstairs, upstairs, running. The human activity recognition performance of our 2D-CNN based method which shows 93.17% accuracy.

References

  1. Gallagher S. Smartphone Sensor Data Mining for Gait Abnormality Detection. Fordham University, New York. 2014, Feb;2.
  2. Kuspa, Kai, and Tony Pratkanis. Classification of mobile device accelerometer data for unique activity identification. Stanford Center for Professional Development (2013).
  3. Kwapisz, Jennifer R., Gary M. Weiss, and Samuel A. Moore. Activity recognition using cell phone accelerometers. ACM SigKDD Explorations Newsletter 12, no. 2 (2011): 74-82.
  4. Zeng, Ming, Le T. Nguyen, Bo Yu, Ole J. Mengshoel, Jiang Zhu, Pang Wu, and Joy Zhang. "Convolutional neural networks for human activity recognition using mobile sensors." In 6th International Conference on Mobile Computing, Applications and Services, pp. 197-205. IEEE, 2014.
  5. Gochoo, Munkhjargal, Tan-Hsu Tan, Shing-Hong Liu, Fu-Rong Jean, Fady S. Alnajjar, and Shih-Chia Huang. Unobtrusive activity recognition of elderly people living alone using anonymous binary sensors and DCNN. IEEE journal of biomedical and health informatics 23, no. 2 (2018): 693-702.
  6. Mathe, Eirini, Apostolos Maniatis, Evaggelos Spyrou, and Phivos Mylonas. A deep learning approach for human action recognition using skeletal information. In GeNeDis 2018, pp. 105-114. Springer, Cham, 2020.
  7. Nweke, Henry Friday, Ying Wah Teh, Mohammed Ali Al-Garadi, and Uzoma Rita Alo. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications 105 (2018): 233-261.
  8. Baccouche, Moez, Franck Mamalet, Christian Wolf, Christophe Garcia, and Atilla Baskurt. Sequential deep learningfor human action recognition. In International workshop on human behavior understanding, pp. 29-39. Springer, Berlin, Heidelberg, 2011.
  9. Liu, Zhi, Chenyang Zhang, and Yingli Tian. 3D-based deep convolutional neural network for action recognition with depth sequences. Image and Vision Computing 55 (2016): 93-100
  10. Jalal, Ahmad, Md Zia Uddin, and T-S. Kim. Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home. IEEE Transactions on Consumer Electronics 58, no. 3(2012): 863-871.
  11. Liu, Zhi, Xin Feng, and Yingli Tian. An effective view and time-invariant action recognition method based on depthvideos. In 2015 Visual Communications and Image Processing (VCIP), pp. 1-4. IEEE, 2015.
  12. Liu, Mengyuan, Hong Liu, and Chen Chen. Enhanced skeleton visualization for view invariant human action recognition. Pattern Recognition 68 (2017): 346-362.
  13. Khan, Zafar A., and Won Sohn. Abnormal human activity recognition system based on R-transform and kernel discriminant technique for elderly home care. IEEE Transactions on Consumer Electronics 57, no. 4 (2011): 1843-1850
  14. Ji, Shuiwang, Wei Xu, Ming Yang, and Kai Yu.3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence 35, no. 1 (2012): 221-231.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-08-30

Issue

Volume 6, Issue 4, July-August-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S. Karthickkumar, Dr. K. Kumar, " Deep Learning for Human Action Recognition with Convolution Neural Network" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.376-380, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT206466