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.
Highlights
Over the past decade, the world is entering into an elders' society
The popular technique used for training and testing model for classifying the activities is Backpropagation Neural Networks; that has already been proposed in the previous work [7]
The paper consists of 7 parts: introduction, a review of literature, human activity classification, system framework, implementation, results, and future works
Summary
The world is entering into an elders' society. As a result, the growth rate of the reproductive population is lower and the range of older people is getting longer. The increasing of the elderly impacts Thailand in many dimensions, such as public policy and law; especially in the health service policy and research on smart home system Their house while their children are working in town. The popular technique used for training and testing model for classifying the activities is Backpropagation Neural Networks; that has already been proposed in the previous work [7] (the original source code was written by James McCaffrey [8], [9]). This technique can use raw data in time format (Time Domain) and it is not necessary to convert data to other data type before learning and classifying. The paper consists of 7 parts: introduction, a review of literature, human activity classification, system framework, implementation, results, and future works
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