**Conflict of interest**

The authors declare no conflict of interest.

*Breathing Monitoring and Pattern Recognition with Wearable Sensors DOI: http://dx.doi.org/10.5772/intechopen.85460*

*Wearable Devices - The Big Wave of Innovation*

very complex ANN, to achieve the best classification.

studied and are a crucial step for a study development.

The authors declare no conflict of interest.

provided to the algorithm.

sensors [10, 25, 55].

**Acknowledgements**

financial support.

**Conflict of interest**

**6. Conclusion**

For example, the application of an ANN in talking [10, 25] recognition through

The neural networks can also be more complex, which depends of the problems intricacies. Aiming to recognize activity patterns such as respiratory effort, using a wearable piezo sensor [25] it was applied networks with up to 17 layers and inputs, a

Wearable devices for breathing monitoring and pattern detection are not simple devices. They must not interfere with the respiratory system activities and need to be highly immune to external perturbations. The understanding of the respiratory mechanics is crucial to the development of wearable sensors, and to know how to connect them in an optimal way. The methods, whether manual processing or the use of machine learning algorithms, depend substantially on the type of the signal

The authors thank CAPES (Coordenação de Aperfeiçoamento de Pessoal

Científico e Tecnológico do Estado do Paraná, and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico), Brazil, for the scholarships and the

de Nível Superior), Fundação Araucária de Apoio ao Desenvolvimento

Overall, the use of machine learning has become increasingly common in health implementations and has proved a very beneficial tool in classifying and recognizing respiratory activities and patterns when combined with wearable

respiratory patterns [10] is of supervised learning as the targets to classify are

**66**
