| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 255.36 KB | Adobe PDF |
Orientador(es)
Resumo(s)
The recognition of Activities of Daily Living (ADL) using the sensors available in
off-the-shelf mobile devices with high accuracy is significant for the development of their framework.
Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature
extraction, data fusion, and data classification was proposed. However, the results may be improved
with the implementation of other methods. Similar to the initial proposal of the framework, this paper
proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going
downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen,
library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest
neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best
machine learning method for ADL and environment recognition. The results obtained show that IBk
and AdaBoost reported better results, with complex data than the deep neural network methods.
Descrição
Palavras-chave
Activities of daily living AdaBoost Mobile devices Artificial neural networks Deep neural networks