Abstract—Activity Analysis Systems or Activity Recognition Systems for the elderly is recently a part of the smart home systems design. This assisted system normally helps the senior people to live alone in a house, safely and improve a quality of life. Therefore, learning to recognize which activities are safe is necessary for classifying the activities of the elderly. This information will give the researchers in the assistive technology some insights to understand the basic daily lives of the elderly. Moreover, it is also help the caregivers to monitor activities of the senior people while they live alone in the house. In this paper, the novel method for detecting and recognizing the activities using Backpropagation Neural Networks has been proposed. The proposed model was tested on a set of basic daily activities (lie, stand, sit, walk and dine). The proposed model was trained to construct the Backpropagation Neural Networks model and used the trained model to classify basic daily activities of the elderly. The proposed model gives the results of 0.78, 0.72 and 0.74 of precision, recall and F1 score, respectively. The discussion and future extension are also given in this paper.
Index Terms—Activity classification, activity analysis systems, activity recognition systems, backpropagation neural networks, smart home technology.
Porawat Visutsak is with the Department of Computer and Information Science, Faculty of Applied Science, KMUTNB, Bangkok, Thailand (e-mail: porawatv@kmutnb.ac.th).
Cite: Porawat Visutsak, "Activity Classification Using Backpropagation Neural Networks for the Daily Lives of the Elderly," International Journal of Machine Learning and Computing vol. 11, no. 3, pp. 188-193, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).