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Physiological informatics: Collection and analyses of data from wearable sensors and smartphone for healthcare

  • Jinwei Bai
  • , Li Shen
  • , Huimin Sun
  • , Bairong Shen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

30 Scopus citations

Abstract

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.

Original languageEnglish
Title of host publicationAdvances in Experimental Medicine and Biology
PublisherSpringer New York LLC
Pages17-37
Number of pages21
DOIs
StatePublished - 2017
Externally publishedYes

Publication series

NameAdvances in Experimental Medicine and Biology
Volume1028
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019

Keywords

  • Data mining for healthcare
  • Participatory medicine
  • Physiological informatics
  • Smartphone
  • Wearable sensors

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