Abstract

Internet of things in clinical domain has opened up new possibilities in remote monitoring of patients by connecting healthcare bio-sensor systems over the internet. This paper has proposed a working prototype of a real-time health monitoring system, which collects sensor data from body area network and communicates the data to a predictive model that is trained on historical clinical data. The prototype is equipped with Analog DeviceTM AD 8232 module for electrocardiogram and heart rate monitoring. CYPRESS CY8CKIT-042-BLE-A PSoC® 4 Bluetooth® Low Energy Pioneer Kit is used for implementation of a body area network, which collects patient’s vitals and communicates the sensor data to a Raspberry Pi3. The gateway device between WPAN (Bluetooth® Low Energy) and WLAN (IEEE 802.11n) is implemented using Raspberry Pi3. The gateway device collects the sensor data over a Bluetooth personal area network coming from all the connected devices and the data is acquired over internet server. ECG- ST wave and heart rate data are sent to the cloud server from the sensors. On the server, a machine learning model is deployed to predict any malfunctions based on sensor readings posted from the real-time health monitoring system and generate early alerts. We have obtained >90% prediction accuracy with random forest classifier using the UCI heart diseases repository.

Keyword

Internet of things, Machine learning, Body area network, Analog deviceTM AD 8232, Electrocardiogram, CYPRESS CY8CKIT-042-BLE-A PSoC® 4 Bluetooth®, Raspberry Pi3, Cloud server.

Cite this article

Mishra A, Mohapatro M

Refference

[1][1]Baker SB, Xiang W, Atkinson I. Internet of things for smart healthcare: technologies, challenges, and opportunities. IEEE Access. 2017; 5:26521-44.

[2][2]Li C, Hu X, Zhang L. The IoT-based heart disease monitoring system for pervasive healthcare service. Procedia Computer Science. 2017; 112:2328-34.

[3][3]Jurik AD, Weaver AC. Remote medical monitoring. Computer. 2008; 41(4):96-9.

[4][4]Oresko JJ, Jin Z, Cheng J, Huang S, Sun Y, Duschl H, et al. A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing. IEEE Transactions on Information Technology in Biomedicine. 2010; 14(3):734-40.

[5][5]Thai DT, Minh QT, Phung PH. Toward an IoT-based expert system for heart disease diagnosis. CEUR Workshop Proceedings. 2017:157-164.

[6][6]Austin PC, Tu JV, Ho JE, Levy D, Lee DS. Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes. Journal of Clinical Epidemiology. 2013; 66(4):398-407.

[7][7]Fiaidhi J, Mohammed S. Digital health in the era of extreme automation. IT Professional. 2018; 20(3):90-5.

[8][8]Hossain MS, Muhammad G. Emotion-aware connected healthcare big data towards 5G. IEEE Internet of Things Journal. 2018; 5(4):2399-406.

[9][9]Alam MM, Malik H, Khan MI, Pardy T, Kuusik A, Le Moullec Y. A survey on the roles of communication technologies in IoT-based personalized healthcare applications. IEEE Access. 2018; 6:36611-31.

[10][10]Chen X, Rhee W, Wang Z. Low power sensor design for IoT and mobile healthcare applications. China Communications. 2015; 12(5):42-54.

[11][11]Yang P, Stankevicius D, Marozas V, Deng Z, Liu E, Lukosevicius A, et al. Lifelogging data validation model for internet of things enabled personalized healthcare. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018; 48(1):50-64.

[12][12]Salahuddin MA, Al-Fuqaha A, Guizani M, Shuaib K, Sallabi F. Softwarization of internet of things infrastructure for secure and smart healthcare. Computer. 2018; 50(7):74-9.

[13][13]He S, Cheng B, Wang H, Huang Y, Chen J. Proactive personalized services through fog-cloud computing in large-scale IoT-based healthcare application. China Communications. 2017; 14(11):1-16.

[14][14]Muhammed T, Mehmood R, Albeshri A, Katib I. UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access. 2018; 6:32258-85.

[15][15]Wu T, Wu F, Redouté JM, Yuce MR. An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access. 2017; 5:11413-22.

[16][16]Guo J. Smartphone-powered electrochemical biosensing dongle for emerging medical IoTs application. IEEE Transactions on Industrial Informatics. 2018; 14(6):2592-7.

[17][17]Mahdiani S, Jeyhani V, Peltokangas M, Vehkaoja A. Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis? In international conference of the engineering in medicine and biology society 2015 (pp. 5948-51). IEEE.

[18][18]Holzinger A. Machine learning for health informatics: state-of-the-art and future challenges. Springer; 2016.

[19][19]Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research. 2011; 2825-30.

[20][20]http://archive.ics.uci.edu/ml/index.php. Accessed 25 November 2018.

[21][21]https://archive.ics.uci.edu/ml/datasets/Heart+Disease. Accessed 25 November 2018.

[22][22]Shahul S, Suneel S, Rahaman MA, Swathi JN. A study of data pre-processing techniques for machine learning algorithm to predict software effort estimation. Imperial Journal of Interdisciplinary Research. 2016; 2(6): 546-50.

[23][23]Aparna UR, Paul S. Feature selection and extraction in data mining. In online international conference on green engineering and technologies 2016 (pp. 1-3). IEEE.

[24][24]Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for medical diagnosis: an overview of heart disease prediction. International Journal of Computer Applications. 2011; 17(8):43-8.

[25][25]Biau G. Analysis of a random forests model. Journal of Machine Learning Research. 2012; 13: 1063-95.

[26][26]Gupta B, Rawat A, Jain A, Arora A, Dhami N. Analysis of various decision tree algorithms for classification in data mining. International Journal of Computer Applications. 2017; 8:15-9.