TY - CHAP
T1 - Physiological informatics
T2 - Collection and analyses of data from wearable sensors and smartphone for healthcare
AU - Bai, Jinwei
AU - Shen, Li
AU - Sun, Huimin
AU - Shen, Bairong
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Data mining for healthcare
KW - Participatory medicine
KW - Physiological informatics
KW - Smartphone
KW - Wearable sensors
UR - https://www.scopus.com/pages/publications/85032331629
U2 - 10.1007/978-981-10-6041-0_2
DO - 10.1007/978-981-10-6041-0_2
M3 - Chapter
C2 - 29058214
AN - SCOPUS:85032331629
T3 - Advances in Experimental Medicine and Biology
SP - 17
EP - 37
BT - Advances in Experimental Medicine and Biology
PB - Springer New York LLC
ER -