Chapter title |
Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare
|
---|---|
Chapter number | 2 |
Book title |
Healthcare and Big Data Management
|
Published in |
Advances in experimental medicine and biology, January 2017
|
DOI | 10.1007/978-981-10-6041-0_2 |
Pubmed ID | |
Book ISBNs |
978-9-81-106040-3, 978-9-81-106041-0
|
Authors |
Jinwei Bai, Li Shen, Huimin Sun, Bairong Shen |
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. |
X Demographics
Geographical breakdown
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United States | 1 | 50% |
France | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 49 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Bachelor | 9 | 18% |
Researcher | 7 | 14% |
Student > Ph. D. Student | 6 | 12% |
Student > Doctoral Student | 4 | 8% |
Student > Master | 3 | 6% |
Other | 7 | 14% |
Unknown | 13 | 27% |
Readers by discipline | Count | As % |
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Computer Science | 10 | 20% |
Medicine and Dentistry | 9 | 18% |
Biochemistry, Genetics and Molecular Biology | 3 | 6% |
Business, Management and Accounting | 3 | 6% |
Psychology | 3 | 6% |
Other | 7 | 14% |
Unknown | 14 | 29% |