Meaningful Data Treatment from Multiple Physiological Sensors in a Cyber-Physical System

Miranda, J. A. and Canabal, M. F. and Lanza-Gutiérrez, José Manuel and Portela-García, M. and López-Ongil, C. and Riesgo Alcaide, Teresa (2017). Meaningful Data Treatment from Multiple Physiological Sensors in a Cyber-Physical System. In: "DCIS 2017: XXXII Conference on Design of Circuits and Integrated Systems", 22nd-24th November 2017, Barcelona (Spain). pp. 100-104.

Description

Title: Meaningful Data Treatment from Multiple Physiological Sensors in a Cyber-Physical System
Author/s:
  • Miranda, J. A.
  • Canabal, M. F.
  • Lanza-Gutiérrez, José Manuel
  • Portela-García, M.
  • López-Ongil, C.
  • Riesgo Alcaide, Teresa
Item Type: Presentation at Congress or Conference (Article)
Event Title: DCIS 2017: XXXII Conference on Design of Circuits and Integrated Systems
Event Dates: 22nd-24th November 2017
Event Location: Barcelona (Spain)
Title of Book: DCIS 2017: XXXII Conference on Design of Circuits and Integrated Systems
Date: 2017
Subjects:
Freetext Keywords: Smart Sensors; Wireless Sensor Networks; Machine Learning
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Once specific Smart Sensors are designed and manufactured in the newest nanoelectronics technology, and a Wireless Sensor Network is designed for being used on wearable applications (Cyber-Physical System) with optimum performance (data rate, power consumption, comfortability, etc.) the next step is the treatment applicable to the large amount of data collected. This can be a very general, and sometimes an unaffordable problem, but considering a system collecting physiological data from smart sensors on a human body, the range of possibilities is restricted to health or leisure but also to safety. In this case, a finite, and well-located, number of physiological sensors are producing few data per unit of time, which are locally processed for obtaining a reduced set of characteristics that are globally analyzed. In this paper, an analysis on different approaches for combining data from smart sensors attached to human body, with the purpose of determining the main emotion present in the person, is presented. Machine learning, selection of the best characteristics from raw sensor data, databases for system training, etc. are the key aspects in this problem. The conclusions of the analysis will help in the design of a new application, where emotion detection can be used for personal safety (domestic violence, sexual violence, bullying, etc.). Attention is paid on the locally and globally data processing in terms of hardware and software, together with low-power behavior.

More information

Item ID: 51130
DC Identifier: http://oa.upm.es/51130/
OAI Identifier: oai:oa.upm.es:51130
Official URL: https://dcis2017.upc.edu/en
Deposited by: Memoria Investigacion
Deposited on: 22 Nov 2018 16:00
Last Modified: 05 Jun 2019 14:46
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