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Wearable Sensor Signals: An Overview of the AI Models Most Commonly Applied to Time Series Data Analysis

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Connected e-Health

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1021))

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Abstract

Nowadays, wearable technology represents a valid, autonomous and non-invasive instrument to capture, analyze and collect physiological data. Several time series signals, such as electrocardiographic or electromyographic signals, can, in fact, be acquired anytime and anywhere, providing the least possible discomfort to the patient, thanks to the continuous development of increasingly advanced devices. While the unceasing acquisition of data contributes to the improvement of the patient care process, the sheer volume of the resulting data makes the analysis and processing of such data difficult and particularly burdensome. The integration of wearable sensors with Artificial Intelligence contributes to the realization of faster, more easily applied and more cost-effective solutions aimed at improving early diagnosis, personalized treatment, remote patient monitoring and better decision making with a consequent reduction in healthcare costs. The increase in the use and application of these techniques, together with the continuous development of new models, raises the question of which technique is the most reliable and accurate in the analysis of such data, in addition to rendering the information explainable and understandable. The black box nature of many algorithms has, in fact, reduced their application in some sectors, such as healthcare, where the understandability and explainability of the results obtained are necessary in order to gain the trust of medical experts and patients. This chapter presents an overview of the main Artificial Intelligence models used for time series data analysis, highlighting the main characteristics of each. The aim is to provide researchers with an panoramic that can guide them in choosing the most suitable technique for their studies.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Physics, University of Campania “Luigi Vanvitelli”, Viale Lincoln, 5, Caserta, 81100, Italy

    Laura Verde

  2. Institute of High Performance Computing and Networking, National Research Council of Italy, Via Pietro Castellino 111, Naples, 80131, Italy

    Giovanna Sannino

Authors
  1. Laura Verde
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  2. Giovanna Sannino
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Corresponding author

Correspondence to Giovanna Sannino .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Sushruta Mishra

  2. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. KIET Group of Institutions, Ghaziabad, Delhi-NCR, India

    Akash Kumar Bhoi

  4. School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  5. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Instituto de Investigación Biomédica de Salamanca, Salamanca, Spain

    Juan M. Corchado

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Verde, L., Sannino, G. (2022). Wearable Sensor Signals: An Overview of the AI Models Most Commonly Applied to Time Series Data Analysis. In: Mishra, S., González-Briones, A., Bhoi, A.K., Mallick, P.K., Corchado, J.M. (eds) Connected e-Health. Studies in Computational Intelligence, vol 1021. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_7

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