IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts
Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, and evidence...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Online Access: | https://ieeexplore.ieee.org/document/9361371/ |
| _version_ | 1818727589799264256 |
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| author | Qingxue Zhang Vincenzo Piuri Edward A. Clancy Dian Zhou Thomas Penzel Wenchuang Walter Hu Hui Zheng |
| author_facet | Qingxue Zhang Vincenzo Piuri Edward A. Clancy Dian Zhou Thomas Penzel Wenchuang Walter Hu Hui Zheng |
| author_sort | Qingxue Zhang |
| collection | DOAJ |
| description | Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, and evidence-based medical decision support. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data and nonlinearly convert the patterns to high-level medical insights. |
| first_indexed | 2024-12-17T22:16:30Z |
| format | Article |
| id | doaj.art-13b9ef15c02241e5a5f714fdd4ecbeb3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T22:16:30Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-13b9ef15c02241e5a5f714fdd4ecbeb32022-12-21T21:30:36ZengIEEEIEEE Access2169-35362021-01-019304523045510.1109/ACCESS.2021.30575289361371IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big ImpactsQingxue Zhang0https://orcid.org/0000-0001-7125-7928Vincenzo Piuri1Edward A. Clancy2Dian Zhou3Thomas Penzel4Wenchuang Walter Hu5Hui Zheng6Department of Electrical and Computer Engineering, Indiana University Purdue University, Indianapolis, IN, USADepartment of Computer Engineering, University of Milan, Milan, ItalyDepartment of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USADepartment of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USAInterdisciplinary Center of Sleep Medicine, Charite University Hospital, Berlin, GermanyDepartment of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX, USADepartment of Medicine, Biostatistics Center, Harvard University, Cambridge, MA, USASmart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, and evidence-based medical decision support. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data and nonlinearly convert the patterns to high-level medical insights.https://ieeexplore.ieee.org/document/9361371/ |
| spellingShingle | Qingxue Zhang Vincenzo Piuri Edward A. Clancy Dian Zhou Thomas Penzel Wenchuang Walter Hu Hui Zheng IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts IEEE Access |
| title | IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts |
| title_full | IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts |
| title_fullStr | IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts |
| title_full_unstemmed | IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts |
| title_short | IEEE Access Special Section Editorial: Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts |
| title_sort | ieee access special section editorial smart health sensing and computational intelligence from big data to big impacts |
| url | https://ieeexplore.ieee.org/document/9361371/ |
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