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Abstract(s)
This dissertation reviews Internet of Things concepts and implementations, state-of-the-art technology with practical examples, as well as data fusion methods applied in different problems. The
purpose of this study is to review different data fusion methods and develop a system to provide
recognition of human activity that can be applied in day care homes and in hospitals to monitor
patients.
The system’s objective is to study human activity recognition based on the data recovered by sensors like accelerometers and gyroscopes. In order to transform this data to useful information and
practical results to monitoring patients with accuracy and high performance, two different neural
networks were implemented.
To conclude, the results from the two different neural networks are compared to each other and
compared with systems from other authors. It is hoped this study will inform other authors and
developers about the performance of neural networks when managing human activity recognition
systems.
Description
Keywords
Data Fusion Deep Learning Health-Care Human Activity Recognition Internet of Things Neural Networks Smart Home