Air Care – Machine Learning Approach to Develop a Supportive and Monitoring System for an Elder

Abstract

Elderly people are one of the greatest assets of our nation, and it suits all the countries. Due to the improper way of living, bad health habits, and because of many other factors, they need to be monitored. In these days of our lives, people are coming up with a lot of tools and technologies, but none of them suggested a good, sophisticated approach. Both human and technology has some downsides by themselves. This creates the need for an application that can fill all the downsides of the available approaches. Air Care is a cost-efficient, minimalistic, sophisticated approach to monitor elders by their activities. Mainly targets on loco-motions, high-level activities, restricting their motions, monitoring people who visit home and notify, and monitoring the attentiveness of caregivers like so. Accuracy rate of 89% gained through the process. The primary objective of our research project is to find a solution using Machine learning, and deep learning to overcome the monitoring issues. Therefore, this paper focuses on developing and deploying a monitoring application by clustering data and using algorithms (SVM & K-Means) and neural network in a minimalistic way.

Country : Sri Lanka

1 Mathushan Shanmugathashan2 Sivagnanasundaram Naveen3 Mithusha Kamaleswaran4 Dasun Maduranga Weerasinghe5 K.B.A.B. Chathurika

  1. Department of Computer Science and Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Computer Science and Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Computer Science and Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Computer Science and Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  5. Department of Computer Science and Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 6, Issue 12, December 2022 pp. 5-11

doi.org/10.47001/IRJIET/2022.612002

References

  1. R. Manus , A. Corey, J. W. Graves,, J. H. Campbell and Kim, "Using Support Vector Machines to Classify Student Attentivenss for the Development of Personalized Learning Systems," in ResearchGate, 2013.
  2. W. Gauswami, and K. Trivedi, "Implementation of Machine Learning for Gender Detection using CNN on rashperry Pi Platform," in 2nd International Conference on Inventive System and Control ICISC 2018, 2018.
  3. H. Y. Emny, H. Yaya and Lukas, "Comparision of Data Mining Classification Algorithms for Student Performance," in IEEE Xplore, 2022.
  4. C. Corinna and V. Vladimir, "Support- Vector Networks," in 273-297 Machine Learning, 1995.
  5. J. MacQueen, "Some methods for classification and analysis of multiverse observations," in 5th Berkeley Symposium on Mathematical Statistics and Probabality , University of California Press, 1967.
  6. H. L. Wagner, , C. J. MacDonald and A. S. Manstead, "Communication of Individual Emotions by Spontaneous Facial Expressions," in Journal of Personality and Social Psychology, 1986.
  7. P. Shanmugavadivu and K. Ashish, "Rapid face Detection and annotation with loosely face Geometry," in 2016 2nd International Conference on Contemporary Computing Informatics, 04 May 2017.
  8. D. Kriti and S. Shanu, "Review and Comparison of face detection algorithms," in 2017 7th International Conference on Cloud Computing, Data Science and Engineering - Confluence, 12 January 2017.
  9. C. Yang, Y. Junsong and T. Yandong, "Video Anomaly Search in Crowded Scenes via Spatio - Temporal Motion Context," in IEEE Transactions on Information Forensics and Security, 2013.
  10. R. Xiaofeng and M. Jetendra, "Learning a Classification model for segmentation," in University of California at Berkeley, Berkeley CA 94720.
  11. W. Pichao, L. Wanqing, O. Philip, Jun Wan and E. Sergio, "RGB-D based human motion recognition with deep learning," in Computer vision and image understanding, June 2018.
  12. P. Viola and J. M, "Robust real-time object detection," in Int. Journ. Of Comp. Vis. vol 57, 2004.
  13. A.Bulling, U. Blanke and B. Schiele, "A tutorial on human activity recoginition using body-worn inertial sensors," in Collection and curation of a large reference dataset for activity recognition, In IEEE International conference on system, man and cybernetics, 2011.
  14. C. S. Chan, S. E. Slaughter , C. A. Jones, C. Ickert and A. S. Wagg, "Measuring activity performance of older adults using the activitPAL: rapid review," in The opportunity challenge: A benchmark database for on-body sensor-based activity recognition., 2017.
  15. W. Moshu, S. Guangdu and Z. Jun, "A robust systems of face detection and precise face organ location," in IEEE, Harbin, China, 2011.
  16. H. Deny-Yuan , H. Chun-Ying, H. Wu-Chil and L. Ta-Wei, "Face detection based on features analysis and edge detection against skin color-like backgrouns," in IEEE, Shenzhen, China, 17 February 2011.
  17. Q. Junfeng , M. Shiewei, H. Zhonghua and S. Yujie, "Face Detection and recongition methods based on skin color and depth information," in IEEE, Xianning, China, 16 May 2011.
  18. G. Nawras and B. J. Regine Le, "Proposal of a remote monitoring system for elderly health prevention," in IEEE, Sfax, Tunisia, 2017 October 19.
  19. A.Raafat, S. Assim, K. Loay Taha, A.-S. Abdulla Mohammad, S. Hussain and Eisa Sajwani, "Remote monitoring framework for eldery care home centers in UAE," in IEEE, Shenzhen, China, 2021 April 14.
  20. F. Michal and T. Lukasz, "Health monitoring system for protecting elderly people," in IEEE, Split, Croatia, 2006 September 01.
  21. Y. Ching Yee and S. Rubita, "Motion Detection and Analysis with Four Different Detectors," in 2011 Third International Conference on Computational Intelligence, Modelling & Simulation, Langakawi, Malaysia, 2011.