Literature DB >> 29058214

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare.

Jinwei Bai1, Li Shen2, Huimin Sun3, Bairong Shen4.   

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.

Keywords:  Data mining for healthcare; Participatory medicine; Physiological informatics; Smartphone; Wearable sensors

Mesh:

Year:  2017        PMID: 29058214     DOI: 10.1007/978-981-10-6041-0_2

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   2.622


  7 in total

1.  Pilot Behavior Recognition Based on Multi-Modality Fusion Technology Using Physiological Characteristics.

Authors:  Yuhan Li; Ke Li; Shaofan Wang; Xiaodan Chen; Dongsheng Wen
Journal:  Biosensors (Basel)       Date:  2022-06-12

2.  The fourth scientific discovery paradigm for precision medicine and healthcare: Challenges ahead.

Authors:  Li Shen; Jinwei Bai; Jiao Wang; Bairong Shen
Journal:  Precis Clin Med       Date:  2021-04-16

Review 3.  Sensor technology for nursing research.

Authors:  Nancy S Redeker
Journal:  Nurs Outlook       Date:  2020-06-21       Impact factor: 3.315

Review 4.  Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis.

Authors:  Qaisar Abbas; Abdullah Alsheddy
Journal:  Sensors (Basel)       Date:  2020-12-24       Impact factor: 3.576

5.  NDDRF: A risk factor knowledgebase for personalized prevention of neurodegenerative diseases.

Authors:  Cheng Bi; Shengrong Zhou; Xingyun Liu; Yu Zhu; Jia Yu; Xueli Zhang; Manhong Shi; Rongrong Wu; Hongxin He; Chaoying Zhan; Yuxin Lin; Bairong Shen
Journal:  J Adv Res       Date:  2021-06-20       Impact factor: 12.822

6.  Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals.

Authors:  Manhong Shi; Hongxin He; Wanchen Geng; Rongrong Wu; Chaoying Zhan; Yanwen Jin; Fei Zhu; Shumin Ren; Bairong Shen
Journal:  Front Physiol       Date:  2020-02-25       Impact factor: 4.566

Review 7.  Translational Informatics for Parkinson's Disease: from Big Biomedical Data to Small Actionable Alterations.

Authors:  Bairong Shen; Yuxin Lin; Cheng Bi; Shengrong Zhou; Zhongchen Bai; Guangmin Zheng; Jing Zhou
Journal:  Genomics Proteomics Bioinformatics       Date:  2019-11-28       Impact factor: 7.691

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.