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Capacity Control in Indoor Spaces Using Machine Learning Techniques Together with BLE Technology
Journal of Sensor and Actuator Networks ; 10(2):12, 2021.
Article in English | Web of Science | ID: covidwho-1298175
ABSTRACT
At present, capacity control in indoor spaces is critical in the current situation in which we are living in, due to the pandemic. In this work, we propose a new solution using machine learning techniques with BLE technology. This study presents a real experiment in a university environment and we study three different prediction models using machine learning techniques-specifically, logistic regression, decision trees and artificial neural networks. As a conclusion, the study shows that machine learning techniques, in particular decision trees, together with BLE technology, provide a solution to the problem. The contribution of this research work shows that the prediction model obtained is capable of detecting when the COVID capacity of an enclosed space is exceeded. In addition, it ensures that no false negatives are produced, i.e., all the people inside the laboratory will be correctly counted.

Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: Journal of Sensor and Actuator Networks Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: Journal of Sensor and Actuator Networks Year: 2021 Document Type: Article