Activity Recognition on Subject Independent Using Machine Learning

Kee, Y. J.; Shah Zainudin, M. N.; Idris, M. I.; Ramlee, R. H.; Kamarudin, M. R.

Activity Recognition on Subject Independent Using Machine Learning

Cybernetics and Information Technologies

Volume 20 (2020): Issue 3 (September 2020)

By:

Y. J. Kee, M. N. Shah Zainudin, M. I. Idris, R. H. Ramlee and M. R. Kamarudin

Open Access

|Sep 2020

References

1. Topline, F. Core Trends Survey Do You Use the Internet or Email, at Least Occasionally ? – Do You Access the Internet on a Cell Phone, Tablet or Other Mobile Handheld Device, Vol. December 2012, January 2018, pp. 1-9.
Search in Google Scholar Back to article
2. Lim, K. H. Risk Factors of Home Injury among Elderly People in Malaysia. – Asian J. Gerontol. Geriatr., Vol. 9, 2014, No 1, pp. 16-20.
Search in Google Scholar Back to article
3. Cheng, L., Y. Guan, K. Zhu, Y. Li. Recognition of Human Activities Using Machine Learning Methods with Wearable Sensors. – In: Proc. of IEEE 7th Annu. Comput. Commun. Work. Conf. (CCWC’17), 2017.10.1109/CCWC.2017.7868369
Search in Google Scholar Back to article
4. Walse, K. H., R. V. Dharaskar, V. M. Thakare. Performance Evaluation of Classifiers on WISDM Dataset for Human. – Activity Recognition, Vol. January 2016, 2016, pp. 1-7.10.1145/2905055.2905232
Search in Google Scholar Back to article
5. Cufoglu, A., A. Coskun. Testing and Analysis of Activities of Daily Living Data with Machine Learning Algorithms. – Int. J. Adv. Comput. Sci. Appl., Vol. 7, 2016, No 3.10.14569/IJACSA.2016.070359
Search in Google Scholar Back to article
6. Fida, B., I. Bernabucci, D. Bibbo, S. Conforto, M. Schmid. Varying Behavior of Different Window Sizes on the Classification of Static and Dynamic Physical Activities from a Single Accelerometer. – Med. Eng. Phys., Vol. 37, 2015, No 7, pp. 705-711.10.1016/j.medengphy.2015.04.00525983067
Search in Google Scholar Back to article
7. Cleland, I. Optimal Placement of Accelerometers for the Detection of Everyday Activities. – Sensors (Basel)., Vol. 13, 2013, No 7, pp. 9183-9200.10.3390/s130709183375864423867744
Search in Google Scholar Back to article
8. Awan, M. A., Z. Guangbin, H. C. Kim, S. D. Kim. Subject-Independent Human Activity Recognition Using Smartphone Accelerometer with Cloud Support. – Int. J. ad hoc Ubiquitous Comput., Vol. 20, 2015, No 3, 172.10.1504/IJAHUC.2015.073170
Search in Google Scholar Back to article
9. Nabian, M. A Comparative Study on Machine Learning Classification Models for Activity Recognition. – J. Inf. Technol. Softw. Eng., Vol. 7, 2017, No 4, pp. 4-8.
Search in Google Scholar Back to article
10. Kwapisz, J. R., G. M. Weiss, S. A. Moore. Activity Recognition Using Cell Phone Accelerometers. – ACM SIGKDD Explor. Newsl., Vol. 12, 2011, No 2, 74.10.1145/1964897.1964918
Search in Google Scholar Back to article
11. Ravi, N., N. Dandekar, P. Mysore, M. L. Littman. Activity Recognition from Accelerometer Data. – In: Proc. of 15th International Conference on Artificial Intelligence, Florida, 2005.
Search in Google Scholar Back to article
12. Zainudin, M. N. S., Y. J. Kee, M. I. Idris, M. R. Kamarudin, R. H. Ramlee. Recognizing the Activity Daily Living (ADL) for Subject Independent. – International Journal of Recent Technology and Engineering, Vol. 8, 2019, No 3, pp. 5422-5427.10.35940/ijrte.B2381.098319
Search in Google Scholar Back to article
13. Vavoulas, G., C. Chatzaki, T. Malliotakis, M. Pediaditis, M. Tsinakis. The MobiAct Dataset: Recognition of Activities of Daily Living Using Smartphones. – In: Proc. of 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health, 2016.10.5220/0005792401430151
Search in Google Scholar Back to article
14. Venkatesh, B., J. Anuradha. A Review of Feature Selection and its Methods. – Cybernatics and Information Technologies, Vol. 19, 2019, No 1, pp. 3-26.10.2478/cait-2019-0001
Search in Google Scholar Back to article
15. Zainudin, M. N. S., M. N. Sulaiman, N. Mustapha, T. Perumal, R. Mohamed. Recognizing Complex Human Activities Using Hybrid Feature Selections Based on an Accelerometer Sensor. – International Journal of Technology, Vol. 8, 2017, No 5, pp. 968-978.10.14716/ijtech.v8i5.879
Search in Google Scholar Back to article
16. Mohamed, R., T. Perumal, M. N. Sulaiman, N. Mustapha, M. N. S. Zainudin. Modelling Activity Recognition of Multi Resident Using Label Combination of Multi Label Classification in Smart Home. – In: Proc. of 2nd International Conference of Applied Science and Technology (ICAST’17), 2017.10.1063/1.5005427
Search in Google Scholar Back to article

DOI: https://doi.org/10.2478/cait-2020-0028 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702

Journal RSS Feed

Language: English

Page range: 64 - 74

Submitted on: Mar 26, 2020

Accepted on: Jul 27, 2020

Published on: Sep 13, 2020

Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Activity Daily Living (ADL),

Related subjects:

Information technology

© 2020 Y. J. Kee, M. N. Shah Zainudin, M. I. Idris, R. H. Ramlee, M. R. Kamarudin, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Previous article Volume 20 (2020): Issue 3 (September 2020)Next article