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A Machine Learning Approach in Wearable Technologies

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Machine Learning for Advanced Functional Materials

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Abstract

The combination of wearable devices with the Internet of Things (IoT) and machine learning technologies has led to innovative analytical tools with potential applications in different fields, ranging from healthcare to smart agriculture. In this chapter, we provide an overview of the application of machine learning algorithms to wearable technologies. After introducing the algorithms more commonly used for analyzing data from wearable devices, we review contributions to the field within the last 5 years. Special emphasis is placed on the application of this approach to health monitoring, sports analytics, and smart agriculture.

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Acknowlegements

This work was supported by FAPESP (2018/22214-6 and 2020/14906-5), CNPq, CAPES, and INEO.

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Authors and Affiliations

  1. São Carlos Institute of Physics, University of São Paulo, São Carlos, São Paulo, 13560-970, Brazil

    Gisela Ibáñez-Redin, Oscar S. Duarte, Giovana Rosso Cagnani & Osvaldo N. Oliveira

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  1. Gisela Ibáñez-Redin
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  2. Oscar S. Duarte
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  3. Giovana Rosso Cagnani
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  4. Osvaldo N. Oliveira
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Correspondence to Gisela Ibáñez-Redin .

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Editors and Affiliations

  1. University of Sao Paulo, Sao Carlos Institution of Physics, Grupo de Polimeros, São Paulo, Brazil

    Nirav Joshi

  2. Department of Civil Engineering Materials and Structures, Indian Institute of Technology Jammu, Jammu, India

    Vinod Kushvaha

  3. Proof of Concept and Innovation Group, Stanley Black and Decker (United States), Atlanta, GA, USA

    Priyanka Madhushri

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Ibáñez-Redin, G., Duarte, O.S., Cagnani, G.R., Oliveira, O.N. (2023). A Machine Learning Approach in Wearable Technologies. In: Joshi, N., Kushvaha, V., Madhushri, P. (eds) Machine Learning for Advanced Functional Materials. Springer, Singapore. https://doi.org/10.1007/978-981-99-0393-1_3

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