Elderly Daily Activity-Based Mood Quality Estimation Using Decision-Making Methods and Smart Facilities (Smart Home, Smart Wristband, and Smartphone)
Authors
Abstract:
Due to the growth of the aging phenomenon, the use of intelligent systems technology to monitor daily activities, which leads to a reduction in the costs for health care of the elderly, has received much attention. Considering that each person's daily activities are related to his/her moods, thus, the relationship can be modeled using intelligent decision-making algorithms such as machine learning. In this study, to model Mood according to the daily activities of the elderly, intelligent decision-making algorithms such as descriptive Bayesian network-based probabilistic model, support vector machine, K nearest neighbor, linear discriminant analysis, decision tree, and ensemble learning methods, such as Bagging and Boosting was used. For the mentioned purpose, an intelligent system, including smart home, wristband, and smartphone, was designed and prepared to collect the dataset, needed to implement, train, and evaluate the proposed intelligent models. With the intelligent system, an elderly woman's daily activities are recorded for five months and her mood status is labeled by a psychologist team. The obtained results prove that the decision-making methods of the support vector machine and the ensemble Bagging tree can accurately detect the person's moods according to her daily activities. However, in a practical aspect, the designed Bayesian network model is more effective to assist psychologists to analyze the causes of detected mood states, by providing a descriptive relationship between variables related to the daily activities and mood states.
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Journal title
volume 19 issue 2
pages 1- 21
publication date 2022-04
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