Human Activity Classification Using Deep Learning

Authors

  • Irfan Ayoub  Department of Computer Engineering, Government College of Engineering and Technology, Jammu, India
  • Bhawna Sharma  Department of Computer Engineering, Government College of Engineering and Technology, Jammu, India
  • Sheetal Gandotra  Department of Computer Engineering, Government College of Engineering and Technology, Jammu, India
  • Manisha Manhas  Department of Computer Engineering, Government College of Engineering and Technology, Jammu, India
  • Mehak Sharma  Department of Computer Engineering, Government College of Engineering and Technology, Jammu, India
  • Umar Farooq  Department of Computer Engineering, Government College of Engineering and Technology, Jammu, India

DOI:

https://doi.org/10.32628/CSEIT228442

Keywords:

Dataset, Deep Learning, Long Short-Term Memory (LSTM) Model, Tensor Flow.

Abstract

This paper describes a method to classify human activities using accelerometer data by training a deep learning model and using it in an android app which gathers real time accelerometer data while the device is with user and classifies his activity by assigning a probabilistic value with highest probability being the activity predicated. The dataset used in this paper is freely available which is provided by WISDM Lab and Google cloud-based instances running tensor flow library for python to code and train the model.

References

  1. Mart´ın Abadi et al, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems”. Available at: https://www.tensorflow.org  2015.
  2. C. E. Matthews et al, “Amount of time spent in sedentary behaviors and cause-specific mortality in US adults”, American Journal of Clinical Nutrition 2012. Available at: www.ncbi.nlm.nih.gov/pubmed/22218159.
  3. Xiaoyong Chai and Qiang Yang, “Multiple-Goal Recognition from Low-Level Signals”. In: AAAI (2005). URL: http://www.aaai.org /Papers/AAAI/2005/AAAI05-001.pdf.
  4. Trevor Hasting, “The Elements of Statistical Learning.” Available at : https://web.standford.edu/~hastie/Papers/ESLII. pdf.
  5. Panu Korpipa Juha Parkka Miikka Ermes. “Activity Classification Using Realistic Data From Wearable Sensors”. In: IEEE Transactions On Information Technology In Biomedicine, IEEE, 2006. Available at: https://s3.amazonaws.com/academia.edu.documents/12195796/parkka06.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL&Expires=544770601&Signature=QBZmGI0nx4\%2FLEqsaBbGl6Sm1AXQ\%3D&response-content-disposition=inline\%3B\%20filename\%3DActivityClassificationUsingRealistic.pdf
  6. P. T. Katzmarzyk and I-M. Lee. “Sedentary behavior and life expectancy in the USA: A cause-deleted life table analysis”. In: BMJ Open (2012). URL: https://bmjopen.bmj.com/content/2/4/e000828.
  7. Youngwook Kim and Hao Ling. “Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine”. In: IEEE Transactions On Geoscience And Remote Sensing. IEEE, 2009. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4801689&tag=1.
  8. Nie Lanshun Li Jiazhen Ding Renjie Zhan Dechen Chu Dianhui Li Xue Si Xiandong. “Understanding and Improving Deep Neural Network for Activity Recognition”. In: School of Computer Science and Technology,        Harbin Institute of Technology. Available at:: https://arxiv.org/ftp/arxiv/papers/1805/1805.07020.pdf.
  9. B; Aarts R.M Long X.; Yin. “Single-accelerometerbased daily physical activity classification”. In: Proceedings of the EMBC. IEEE, 2009. Available at: https://pure.tue.nl/ws/files/2828549/Metis234421.pdf.
  10. Francis Bach Mark Schmidt Nicolas Le Roux, “Minimizing Finite Sums with the Stochastic Average Gradient”,           2nd ed.Springer.
  11. Andrew Ng. CS229 Course Notes Decision Trees. URL: http://cs229.stanford.edu/notes/rf-notes.pdf.
  12. Thomas Plotz Nils Y. Hammerla1 Shane Halloran2. “Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables”. In: Open Lab, School of Computing Science, Newcastle University, UK. Available at: https://arxiv.org/pdf/1604.08880.pdf.
  13. Rajan Patel. STATS 202 Lecture 19. Available at:   https://web.stanford.edu/class/stats202/content/lec19-cond.pdf.
  14. F. Pedregosa et al. “Scikit-learn: Machine Learning in Python”. In: Journal Of Machine Learning Research 12, pp.2825–2830,2011
  15. Attila Reiss. “PAMAP2 Dataset: Physical Activity Monitoring”. Available at: : https://archive.ics.uci.edu/ml/machinelearning - databases/ 00231/ readme . pdf. UCI Machine Learning dataset.
  16. Attila Reiss and Dider Stricker. “Introducing a New Benchmarked Dataset for Activity Monitoring”. In: IEEE Computer Society Washington (2012). URL: https://dl.acm.org/citation.cfm?id=2358027.

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

Downloads

Published

2022-08-30

Issue

Volume 8, Issue 5, September-October-2022

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]
Irfan Ayoub, Bhawna Sharma, Sheetal Gandotra, Manisha Manhas, Mehak Sharma, Umar Farooq, " Human Activity Classification Using Deep Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 5, pp.64-71, September-October-2022. Available at doi : https://doi.org/10.32628/CSEIT228442