An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

Journal of Medical Systems

Volumn 40, Issue 12, 2016, Pages

  Yang, Zhe ^a   Zhou, Qihao ^a   Lei, Lei ^b   Zheng, Kan ^a   Xiang, Wei ^c  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b BEIJING JIAOTONG UNIVERSITY   (China)

^c JAMES COOK UNIVERSITY   (Australia)

Author keywords

ECG; Health monitoring; IoT cloud; Wearable sensors

Indexed keywords

ADULT; ARTICLE; CLOUD COMPUTING; ELECTROCARDIOGRAPHY MONITORING; HEALTH CARE; HUMAN; HUMAN EXPERIMENT; INTERNET; INTERNET PROTOCOL; MALE; NORMAL HUMAN; PR INTERVAL; QRS INTERVAL; QT INTERVAL; RELIABILITY; RR INTERVAL; SMARTPHONE; WEB BROWSER; WIRELESS COMMUNICATION; YOUNG ADULT; AMBULATORY ELECTROCARDIOGRAPHY; MOBILE APPLICATION; PROCEDURES; REMOTE SENSING; REPRODUCIBILITY; TELEMETRY;

CLOUD COMPUTING; ELECTROCARDIOGRAPHY, AMBULATORY; HUMANS; INTERNET; MOBILE APPLICATIONS; REMOTE SENSING TECHNOLOGY; REPRODUCIBILITY OF RESULTS; SMARTPHONE; TELEMETRY;

EID: 84993971776     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-016-0644-9     Document Type: Article

References (28)

1. Ageing, In: World Health Organization. http://www.who.int/topics/ageing/en/, 2016.
2. Banerjee, A., and Gupta, S., Analysis of smart mobile applications for healthcare under dynamic context changes. IEEE Trans. Mob. Comput. 14:904–919, 2015. doi:10.1109/tmc.2014.2334606.
3. Zhang, Y., and et al., Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. Accepted by IEEE Systems Journal. doi:10.1109/jsyst.2015.2460747, 2015.
4. Zhang, Y., Chen, M., Huang, D., and et al., iDoctor: Hersonalized and professionalized medical recommendations based on hybrid matrix factorization. Fut. Gener. Comput. Syst. 66:30–35, 2017. doi:10.1016/j.future.2015.12.001.
5. Abadi, M., Subramanian, R., Kia, S., and et al., DECAF: MEG-based multimodal database for decoding affective physiological responses. IEEE Trans. Affect. Comput. 6:209–222, 2015. doi:10.1109/taffc.2015.2392932.
6. Kim, H., Kim, S., Van Helleputte, N., and et al., A configurable and low-power mixed signal SoC for portable ECG monitoring applications. IEEE Trans. Biomed. Circ. Syst. 8:257–267, 2014. doi:10.1109/tbcas.2013.2260159.
7. Tseng, C., Coordinator traffic diffusion for data-intensive Zigbee transmission in real-time electrocardiography monitoring. IEEE Trans. Biomed. Eng. 60:3340–3346, 2013. doi:10.1109/tbme.2013.2266373.
8. Miao, F., Cheng, Y., He, Y., and et al., A wearable context-aware ECG monitoring system integrated with built-in kinematic sensors of the smartphone. Sensors 15:11465–11484, 2015. doi:10.3390/s150511465.
9. Tseng, K., Lin, B., Liao, L., and et al., Development of a wearable mobile electrocardiogram monitoring system by using novel dry foam electrodes. IEEE Syst. J. 8:900–906, 2014. doi:10.1109/jsyst.2013.2260620.
10. Friedman, R., Kogan, A., and Krivolapov, Y., On power and throughput tradeoffs of WiFi and Bluetooth in smartphones. IEEE Trans. Mob. Comput. 12:1363–1376, 2013. doi:10.1109/tmc.2012.117.
11. Shneiderman, B., and Plaisant, C., Sharpening analytic focus to cope with big data volume and variety. IEEE Comput. Graph. Appl. 35:10–14, 2015. doi:10.1109/mcg.2015.64.
12. Zhang, Y., Chen, M., Mao, S., and et al., CAP: Community activity prediction based on big data analysis. IEEE Netw. 28:52–57, 2014. doi:10.1109/mnet.2014.6863132.
13. Alonso-Atienza, F., Morgado, E., Fernandez-Martinez, L., and et al., Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans. Biomed. Eng. 61:832–840, 2014. doi:10.1109/tbme.2013.2290800.
14. Sharma, H., and Sharma, K., An algorithm for sleep apnea detection from single-lead ECG using Hermite basis functions. Comput. Biol. Med. 77:116–124, 2016. doi:10.1016/j.compbiomed.2016.08.012.
15. AD8283 Datasheet(PDF) - Analog Devices. In: Alldatasheet.com. http://www.alldatasheet.com/datasheet-pdf/pdf/418481/AD/AD8283.html, 2016.
16. Buenda̱-Fuentes, F., Arnau-Vives, M., Arnau-Vives, A., and et al., High-bandpass filters in electrocardiography: Source of error in the interpretation of the ST segment. ISRN Cardiol. 2012:1–10, 2012. doi:10.5402/2012/706217.
17. STM32F103RC Datasheet(PDF) - STMicroelectronics. In: Alldatasheet.com. http://www.alldatasheet.com/datasheet-pdf/pdf/231959/STMICROELECTRONICS/STM32F103RC.html, 2016.
18. Al-Fuqaha, A., Guizani, M., Mohammadi, M., and et al., Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17:2347–2376, 2015. doi:10.1109/comst.2015.2444095.
19. Phan, T., et al., Cloud databases for Internet-of-things data. In: Proceedings IEEE 2014 International Conference on Internet of Things (iThings), pp. 117–124. Taipei, 2014.
20. Wang, Y., and et al., Design and evaluation of a novel wireless reconstructed 3-lead ECG monitoring system. In: Proceedings IEEE 2013 Biomedical Circuits and Systems Conference (BioCAS), pp. 362–365, Rotterdam. 2013.
21. Gertsch, M., The Normal ECG and its (Normal) variants. In: The ECG Manual, pp. 17–36. London: Springer, 2009.
22. ECGlibrary.com: Normal adult 12-lead ECG. In: Ecglibrary.com. http://www.ecglibrary.com/norm.php, 2016.
23. Palattella, M., Dohler, M., Grieco, A., and et al., Internet of Things in the 5G era: Enablers, architecture, and business models. IEEE J. Select. Areas Commun. 34:510–527, 2016. doi:10.1109/jsac.2016.2525418.
24. Xiong, X., Zheng, K., Xu, R., and et al., Low power wide area machine-to-machine networks: Key techniques and prototype. IEEE Commun. Mag. 53:64–71, 2015. doi:10.1109/mcom.2015.7263374.
25. Lei, L., Kuang, Y., Cheng, N., and et al., Delay-optimal dynamic mode selection and resource allocation in device-to-device communications - part II: Practical algorithm. IEEE Trans. Veh. Technol. 65:3491–3505, 2016. doi:10.1109/tvt.2015.2444791.
26. Hou, L., Zhao, S., Zheng, K., et al., Internet of Things cloud: architecture and implementation. Accepted by IEEE Communications Magazine. arXiv:1609.07712, 2016.
27. Zheng, K., Yang, Z., Zhang, K., and et al., Big data-driven optimization for mobile networks toward 5G. IEEE Netw. 30:44–51, 2016. doi:10.1109/mnet.2016.7389830.
28. He, S., Chen, J., and et al., Mobility and intruder prior information improving the barrier coverage of sparse sensor networks. IEEE Trans. Mob. Comput. 13:1268–1282, 2014. doi:10.1109/tmc.2013.129.