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React-Native Based Mobile App for Biosignal Data Acquisition Design


Affiliations
1 Ph.D. Student, Department of Computer Science, IICSE University, Inc., USA., United States
 

Human-machine Interfaces (HMIs) is a scientific asset to the interdisciplinary research area of biomedical signal acquisition system and graphical display. Image acquisition from scanning devices has raised great technological challenges to be resolved in the past decade. The proposed mobile application is compatible with both android and iOS devices and it was developed in the React Native framework using Visual Studio code and Expo Snack tools. Biomedical instruments are used to assist doctors in their diagnosis and surgical procedures. More such products in the market have become a recent trend in the form of wearable and handheld portable devices compatible with mobile phones to assist remote patient analysis and recovery process. The signal conditioning preprocessing circuitry designed to provide the graphical information of the biomedical image acquisition devices uses low power, low bandwidth Analog-toDigital converter (ADC). The proposed mobile application is an initial prototype to assist the design of a single-bit ADC using different CMOS technology nodes in addition to displaying a graphical output of ECG.

Keywords

Human-Computer Interface, Transducer Signal Conditioning Circuitry, Analog-to-Digital Converter, Biopotential Signal Processing, Sampling, Biomedical Data Acquisition, Electrocardiogram, React-Native Framework for Mobile App.
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  • Lee, S.hyun. Hochheiser, H. and Valdez, R.S., 2020. Human-computer interaction, ethics, and biomedical informatics. Yearbook of Medical Informatics, 29(01), pp.093-098.

  • Imhoff, M (2002). "Health Informatics". Evaluating Critical Care: 255– 256. doi:10.1007/978-3-642-56719-3_18. ISBN 978-3-540-42606-6.

  • Ou, H.H., Chen, Y.C. and Liu, B.D., 2006, November. A 0.7-V 10-bit 3μW analog-to-digital converter for implantable biomedical applications. In 2006 IEEE Biomedical Circuits and Systems Conference (pp. 122-125). IEEE.

  • Rahul, J., Sora, M. and Sharma, L.D., 2019. An overview on biomedical signal analysis. Int. J. Recent Technol. Eng, 7(5), pp.206-209.

  • Rangayyan RM. Biomedical signal analysis: a case study approach. New York: Wiley-IEEE Press; (2001).

  • Kacar S, Sakoglu Ü. Design of a novel biomedical signal processing and analysis tool or functional neuroimaging. Comput Methods Programs Biomed (2016):46–57

  • Martinek, R., Ladrova, M., Sidikova, M., Jaros, R., Behbehani, K., Kahankova, R. and Kawala-Sterniuk, A., 2021. Advanced bioelectrical signal processing methods: past, present and future approach—part I: cardiac signals. Sensors, 21(15), p.5186.

  • Grinstead, B. and Parten, M.E., 1998, June. Biomedical signal acquisition using" Labview". In Proceedings. 11th IEEE Symposium on Computer-Based Medical Systems (Cat. No. 98CB36237) (pp. 157-161). IEEE.

  • Northrop, R.B., 2003. Analysis and application of analog electronic circuits to biomedical instrumentation. CRC press.

  • Kester, Walt. (2023). MT020: ADC Architectures I: The Flash Converter.

  • Rodríguez-Pérez, A., Delgado-Restituto, M. and Medeiro, F., 2011. Power efficient ADCs for biomedical signal acquisition. Biomedical Engineering, Trends in Electronics, Communications and Software, pp.171-192.

  • https://github.com/DeepthiTchr/BioSignal-Acquisition-System-DesignReact-Native-App

  • Cho, H.J., Lee, J.Y., Park, T.R. and Jwa, J.W., 2022. React Native and Android Mobile Apps for Smart Tourism Information Service to FITs. International Journal of Internet, Broadcasting and Communication, 14(2), pp.63- 69.

  • Baruah, S. and Dutta, J., 2009. Nanotechnology applications in pollution sensing and degradation in agriculture: a review. Environmental Chemistry Letters, 7, pp.191-204.

  • Rai, M., Gade, A., Gaikwad, S., Marcato, P.D. and Durán, N., 2012. Biomedical applications of nano biosensors: the state-of-the-art. Journal of the Brazilian Chemical Society, 23, pp.14-24.

  • Mohammed, J., Lung, C.H., Ocneanu, A., Thakral, A., Jones, C. and Adler, A., 2014, September. Internet of Things: Remote patient monitoring using web services and cloud computing. In 2014 IEEE international conference on internet of things (IThings), and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) (pp. 256-263). IEEE.

  • Lee, B.G. and Chung, W.Y., 2012. A smartphone-based driver safety monitoring system using data fusion. Sensors, 12(12), pp.17536-17552.

  • Ban, S., Lee, Y.J., Kwon, S., Kim, Y.S., Chang, J.W., Kim, J.H. and Yeo, W.H., 2023. Soft Wireless Headband Bioelectronics and Electrooculography for Persistent Human–Machine Interfaces. ACS Applied Electronic Materials.

  • Roncagliolo, P., Arredondo, L. and González, A., 2007, November. Biomedical signal acquisition, processing and transmission using smartphone. In Journal of Physics: Conference Series (Vol. 90, No. 1, p. 012028). IOP Publishing.

  • https://bioelectromagnetism.wordpress.com/ Dr. Ted Burke, School of Electrical and Electronic Engineering, Technological University Dublin–City Campus https://bioelectromagnetism.wordpress.com/2012/11/28/data-from-ecgrecording-in-todays-class/# comment-131

  • Morson, S. and Morson, S., 2014. Using wireframes to design your app. Learn design for iOS development, pp.69-84.

  • https://www.alivecor.com/press/press_release/launch-of-worlds-smallestsingle-lead/

  • https://sanketlife.in/products/pages-sanketlife2-0


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  • React-Native Based Mobile App for Biosignal Data Acquisition Design

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Authors

Deepthi A. J.
Ph.D. Student, Department of Computer Science, IICSE University, Inc., USA., United States

Abstract


Human-machine Interfaces (HMIs) is a scientific asset to the interdisciplinary research area of biomedical signal acquisition system and graphical display. Image acquisition from scanning devices has raised great technological challenges to be resolved in the past decade. The proposed mobile application is compatible with both android and iOS devices and it was developed in the React Native framework using Visual Studio code and Expo Snack tools. Biomedical instruments are used to assist doctors in their diagnosis and surgical procedures. More such products in the market have become a recent trend in the form of wearable and handheld portable devices compatible with mobile phones to assist remote patient analysis and recovery process. The signal conditioning preprocessing circuitry designed to provide the graphical information of the biomedical image acquisition devices uses low power, low bandwidth Analog-toDigital converter (ADC). The proposed mobile application is an initial prototype to assist the design of a single-bit ADC using different CMOS technology nodes in addition to displaying a graphical output of ECG.

Keywords


Human-Computer Interface, Transducer Signal Conditioning Circuitry, Analog-to-Digital Converter, Biopotential Signal Processing, Sampling, Biomedical Data Acquisition, Electrocardiogram, React-Native Framework for Mobile App.

References