Authors:
Linos Nchena
and
Dagmar Janacova
Affiliation:
Department of Automation and Control Engineering, Tomas Bata University in Zlín, Czech Republic
Keyword(s):
Assistive Technologies, Senior Citizen Assistance, Incident Detection System, Health Status Monitor, Artificial Intelligence, Artificial Neural Networks, Machine Learning.
Abstract:
This research paper proposes a monitoring system and a prototype that has been developed for detecting fever status in elderly people or other populations requiring continuous specialist care. With various issues affecting the health of the senior citizens, there is an imperative requirement to have a continuous monitor health status. The monitoring system is beneficial as it will make it feasible to enable the real time detection of fever and thus allowing for the early treatment. Delaying treatment can lead to the underlining health issue going beyond the remediable condition. Thus, quick detection is vital. There are various issues that might causes illness in people. Some of the issues include virus outbreak, seasonal infections, disease, and old age. In this paper our focus is mainly on old age. This group of people is much more at risk of getting ill or frequently need more attention. In this project, the presence of fever or illness has been detected by using artificial intell
igence (AI). The AI technique that is utilized in this project is artificial neural networks. The computation is done by first training the system and then secondly validating the trained system. After the training, the system is supplied with a new set of data, with a known state, to validate that the training was successful. To validate the system, it is provided with sample data to test its efficiency. If the system is well trained the validation data would label that data correctly. That label is known before the validation test, as the sample data had known labels. These known labels were not given to training but not validation system. The system is function properly if its label matched the sample data label. The conducted experiment demonstrated a successful detection with an efficiency rate of 82 percent.
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