Desktop version

Home arrow Computer Science

  • Increase font
  • Decrease font


<<   CONTENTS   >>

REMOTELY ACCESSIBLE MONITORING STATION

As the physiological data are stored at the cloud-based station, they can be easily accessed through remote locations. To retrieve information, Internet connectivity, and a web browser required, and by employing the access credentials, one can retrieve the desired parameters of the patients. Again, to retrieve the data, one needs to establish the communication using HTTP protocol [44]; the stepwise procedure of the data transfer and retrieval is depicted in Figure 7.14.

Confusion matrix of the ANN designed for ECG abnormity

FIGURE 7.13 Confusion matrix of the ANN designed for ECG abnormity.

Procedure for accessing the patient data at the remote station using the HTTP protocol [44]

FIGURE 7.14 Procedure for accessing the patient data at the remote station using the HTTP protocol [44].

PROTOTYPE DEVELOPMENT

To develop the prototype of the above-discussed system, we have integrated the individual modules, as depicted in the sketch diagram represented in Figure 7.15. The output pin of the AD-8232 module that senses the ECG signal is attached to the AO pin of the Arduino Uno, AO pin is an ADC pin, which converts the analog voltage sensed by the AD-8232 into a digital value. All other pins of the AD-8232 and accelerometer connected in the same way, as depicted in the sketch diagram.

Sketch diagram of the HMS and its implantation using Arduino

FIGURE 7.15 Sketch diagram of the HMS and its implantation using Arduino.

The Arduino function is to read and transmit the physiological sensor data over the IoT cloud that written and uploaded into the MCU unit. The DS18B20 temperature sensor is a digital sensor and hence to integrate this with MCU, a different topology used. А 4.7-Ш value register is employed between the data and the VQC pin. The data pin of AD8232 is connected to the digital pin of the MCU, and a function for reading and transmitting the numeric value to “ThingSpeak” is written separately. At last, an Arduino program to connect ESP-8266 to the Internet and to initiate the communication between cloud and hardware is developed, in which both of the above- mentioned functions are called for execution. The program successfully writes the sensor data to the “ThingSpeak” IoT software.

At the IoT platform, as per the above-discussed method, a MATLAB- based processing script is implemented over the live channel data coming through the sensor network, which is visualized to the remote clients with proper authentication.

LIMITATIONS OF THE PROPOSED APPROACH

For any healthcare systems, the essential governing features are the demonstration of its effectiveness, efficiency, and quality to users, the community, and funders. Applying remote medical diagnosis techniques and employing continuous patient monitoring system can help significantly to reduce healthcare costs and conect performance management, specifically in the management of chronic diseases. Particular challenges hi implementation of these systems could be in continuous patient monitoring, threats to patient confidentiality and privacy, poor system design and its implementation, system malfunctioning leading to medical errors and misrepresentation of facts, technology acceptance and lack of system interoperability with electronic health records and other IT tools, decreased face-to-face communication between the doctor and the patient, sudden interruptions of telecommunication networks, seal- ability in terms of data rate, power and energy consumption, antenna design, quality of sendee, energy efficiency, weight of wearable devices, difficulty hi data processing due to the instruments used in patient monitoring, the location of data collected, which affects accuracy and consistency of information, user training to use wearable system, market penetration with device, and sensor type for specific monitoring aspects [28]. All the above limitations need to be addressed to design a robust, comprehensive patient HMS.

CONCLUSION AND FUTURE SCOPE

This chapter introduces the design of smart embedded systems for health monitoring applications, which is an upcoming area in the field of patient healthcare diagnosis and monitoring. It encompasses the concepts starting from the basics of the system design, including the design metrics, to the hardware implementation of the HMS for a better and practical understanding of the system design approach. The first part of the chapter focuses on covering the introductory topics related to the HMSs such as background, history, current trends, and types. This chapter provides a brief oveiview of the smart HMSs and their involved design processes. Further sections are dedicated to concepts that are considered during the design of any system, such as size, cost, speed, etc., and the discussions concerning the improvement of the constraints are provided in this chapter.

Furthermore, discussion is extended to practical aspects of the system design and implementation of a smart system for monitoring ECG, body temperature, and patient activity presented using analysis of the data collected from physiological sensors. This chapter introduces the procedure of developing smart interfaces for health monitoring using concepts of IoT, ANN, and embedded system design, which is an upcoming area presently. In the future, the method is expected to improve for more compact, feasible, and robust systems. In the near future, the intelligent advanced algorithm systems may automatically direct the patient for a particular health issue.

This would be a tremendous medical boon for the patient(s) as well as their clinical care provider. The remote access of the physiological parameters is expected to enhance the diagnostics performance and can improve the life of the patient and their attendants.

ACKNOWLEDGMENT

The authors would like to graciously acknowledge the financial assistance provided by the Department of Biotechnology, Government of India, under Grant BT/PR15673/NER/95/22/2015 dated 09.12.2016.

KEYWORDS

  • smart interfaces
  • comprehensive health monitoring
  • artificial intelligence
  • machine learning
  • personal digital assistant

REFERENCES

  • 1. S. Arduino, Arduino. Arduino LLC, 2015.
  • 2. M. M. Baig and H. Gholamliosseini, “Smart health monitoring systems: An overview of design and modeling,” J. Med. Syst., vol. 37, no. 2, pp. 1-14, 2013.
  • 3. S. P. Balasimdaram, “Increasing the batteiy life of a mobile computing system in a reduced power state through memory compression,” US Patent App. 11/450,214, 2007.
  • 4. J. Basilakis, N. H. Lovell, S. J. Redmond, and B. G. Celler. “Design of a decision- support architecture for the management of remotely monitored patients,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 5, pp. 1216-1226, 2010.
  • 5. S. Brownsell, D. Bradley, S. Blackburn, F. Cardhiaux, and M. S. Hawley, “A systematic review of lifestyle monitoring technologies,” J. Telemed. Telecare, vol. 17. no. 4, pp. 185-189, 2011.
  • 6. M. Chen, Y. Ma, J. Song, C.-F. Lai. and B. Hu, “Smart clothing: Connecting human with clouds and big data for sustainable health monitoring,” Mobile Netw. Appl., vol. 21, no. 5, pp. 825-845,2016.
  • 7. H. T. Cheng and W. Zhuang, "Bluetooth-enabled in-home patient monitoring system: Early detection of Alzheimer's disease,” IEEE Wireless Commit».,vol. 17. no. l.pp. 74-79,2010.
  • 8. S. D. Choudhari and V. Giripunje, “Remote healthcare monitoring system for drivers community based onloTf Int. J. Em erg. Technol. Eng. Res., vol. 4, no. 7, pp. 117-121, 2016.
  • 9. B. Chowdhury and R. Khosla, “RFID-based hospital real-time patient management system,” hi Proc. 6th IEEE/ACISInt. Conf. Comput. Inf Sci., 2007, pp. 363-368.
  • 10. D. C. Classen, S. L. Pestotnik, R. S. Evans, and J. P. Burke, “Computerized surveillance of adverse drug events in hospital patients,” JAMA, vol. 266, no. 20, pp. 2847-2851,1991.
  • 11. H. Costin, C. Rotariu, I. Alexa. G. Constantinescu, V. Cehan, B. Dionisie, G. Andmseac. Y. Felea, E. Crauciuc, and M. Scutariu, “Telemou—a complex system for real-tune medical telemouitoring," in Proc. World Congr. Med. Phys. Biomed. Eng., 2009, pp. 92-95.
  • 12. Adxl335: Small, Low Power, 3-Axis±3 g Accelerometer, Analog Devices, Norwood, MA. USA, 2012.
  • 13. AdS232: Single-Lead, Heart Rate Monitor Front End. Rev. A, Analog Devices, Norwood, MA, USA, 2013.
  • 14. R. Hillestad, J. Bigelow, A. Bower, F. Girosi, R. Meili. R. Scoville, and R. Taylor, “Can electronic medical record systems transform health care? Potential health benefits, savings, and costs,” Health Affairs, vol. 24, no. 5, pp. 1103-1117, 2005.
  • 15. J. J. Hopfield. “Neural networks and physical systems with emergent collective computational abilities,” Proc. Nat. Acad. Sci., vol. 79, no. 8, pp. 2554-2558. 1982.
  • 16. M. Imhoff and S. Kuhls, “Alarm algorithms in critical care monitoring,” Anesth. Analg., vol. 102, no. 5, pp. 1525-1537, 2006.
  • 17. R. S. Istepauian, S. Hu, N. Y. Philip, and A. Stmgoor, “The potential of the Internet of m-health things "rn-IoT" for non-invasive glucose level sensing,” in Proc. Anna. Int. Conf. IEEE Eng. Med. Biol Soc., 2011, pp. 5264-5266.
  • 18. L. Jiug-Jian and X.Hai-Qiao„ “Embedded system in the 21 centuryУ Semicond. Technol., vol. 1,2001.
  • 19. R. Kamal. Embedded Systems: Architecture, Programming, and Design. Noida, India: Tata McGraw-Hill Education, 2011.
  • 20. M. Kikuya, T. Ohkubo, K. Asayama, H. Metoki. T. Obara. S. Saito, J. Haskimoto, K. Totsune, H. Hoshi, and H. Satoh, “Ambulatory blood pressure and 10-year risk of cardiovascular and uon-cardiovascular mortality: The Ohasama study,” Hypertension, vol. 45, no. 2. pp. 240-245, 2005.
  • 21. G. J. Kin, The Architecture of Systems Problem-Solving. New York. NY, USA: Springer Science & Business Media, 2013.
  • 22. S. Kumar, W. J. Nilsen, A. Abemethy, A. Atienza. K. Patrick. M. Pavel, W. T. Riley, A. Shar, B. Spring, and D. Spmijt-Metz, “Mobile health technology evaluation: The mhealth evidence workshop,” Am. J. Prev. Med., vol. 45, no. 2, pp. 228-236, 2013.
  • 23. F. Lau, C. Kuziemsky, M. Price, and J. Gardner. “A review of systematic reviews of health information system studies,” J. Am. Med. Inform. Assoc., vol. 17, no. 6, pp. 637-645,2010.
  • 24. A. M. Law, W. D. Kelton, and W. D. Kelton, Simulation Modeling and Analysis, vol. 2. New York, NY. USA: McGraw-Hill. 1991.
  • 25. C.-C. Lin, M.-J. Chiu, C.-C. Hsiao. R.-G. Lee, and Y.-S. Tsai, “Wireless health care sendee system for the elderly with dementia,” IEEE Trans. Inf. Technol. Biomed., vol. 10, no. 4, pp. 696-704, 2006.
  • 26. G. Lopez, Y Custodio, and J. I. Moreno, “LOBIN: E-textile and wireless-sensor- network-based platform for healthcare monitoring in future hospital environments,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 6. pp. 1446-1458, 2010.
  • 27. M. Mehta, “Esp 8266: A breakthrough in wireless sensor networks and the Internet of things,” Int. J. Electron. Commun. Eng. Technol, vol. 6. no. 1. pp. 07-11, 2015.
  • 28. N. Mohaumiadzadeh and R. Safdari, “Patient monitoring in mobile health: Opportunities and challenges,” Med Arch., vol. 68, no. 1, pp. 57-60, 2014.
  • 29. S. G. Mougiakakou, C. S. Bartsocas. E. Bozas, N. Chaniotakis, D. Iliopoulou, I. Komis, S. Pavlopoulos, A. Prountzou, M. Skevofilakas, and A. Tsoukalis, “Smartdiab: A communication and information technology approach for the intelligent monitoring, management, and follow-up of type 1 diabetes patients,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 3, pp. 622-633, 2010.
  • 30. S. Patel, B.-R. Chen. T. Buckley. R. Rednic. D. McClure, D. Tarsy, L. Shih, J. Dy, M. Welsh, and P. Bonato. “Home monitoring of patients with Parkinson's disease via wearable technology and a web-based application.” in Proc. Amu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2010, pp. 4411-4414.
  • 31. M. Pavier and T. Sammon. “Embedded power management control circuit,” US Patent App. 11/078:807,2005.
  • 32. R. A. Rahman, N. S. A. Aziz, M. Kassim. and. M. I. Yusof, “IoT-based personal health care monitoring device for diabetic patients,” in Proc. IEEE Symp. Comput. Appl: Coinput. Appl. Ind. Election., 2017, pp. 168-173.
  • 33. Programmable Resolution 1-Wire Digital Thennometer Datasheet, Maxim integrated. Sail Jose, CA, USA, 2008.
  • 34. S. Robinson, Simulation: The Practice of ModeI Development and Use. Inglaterra, U.K.: Wiley, 2004.
  • 35. R. Roine. A. Ohinmaa. and D. Hailey, “Assessing telemedicine: A systematic review of the literature,” Can. Med. Assoc. J., vol. 165, no. 6. pp. 765-771, 2001.
  • 36. A. Sangiovanni-Yincentelli, and G. Martin. “Platform-based design and software design methodology for embedded systems,” IEEE Des. Test Comput., vol. 18, no. 6, pp. 23-33,2001.
  • 37. D. Sciuto, F. Salice, L. Pomante, and W. Fomaciari. “Metrics for design space exploration of heterogeneous multiprocessor embedded systems,” in Proc. 10th Int. Symp. HardwJ Sofhv. Codes., 2002, pp. 55-60.
  • 38. A. M. Shams, T. K. Darwish, and M. A. Bayoumi, “Performance analysis of low-power 1-bit CMOS frill adder cells,” IEEE Trans. Extensive Scale Integr. Syst., vol. 10. no. 1, pp. 20-29, 2002.
  • 39. Y. Shim, “Exercise systems in virtual environment,” US Patent App. 12/216,540, 2009.
  • 40. S. Siebig. S. Kulils. M. hnhoff. U. Gather, J. Schohnerich, and С. E. Wrede, “Intensive care unit alarms—How many do we need?” Oit. Care Med., vol. 38, no. 2, pp. 451-456,2010.
  • 41. M. Singh, M. Rajan. V. Shivraj, and P. Balamuralidhar, “Secure MQTT for the Internet of things (IoT),” in Proc. 5th IEEE Int. Conf. Commun. Commun. Syst. Nehv. Technol,
  • 2015, 746-751.
  • 42. P. Swathy, C. Periasamy, “Modular health care monitoring for patients using IoT,” Indian J. Appl Res., vol. 8, no. 4, pp. 21-25, 2018.
  • 43. R. P. Tripathi, G. Mishra. D. Bhatia, and T. K. Siiiha, “Classification of cardiac arrhythmia using hybrid technology of fast discrete Stockwell-trausfonn (FDST) and self-organizing map,” 2018, doi: 10.20944/preprints201806.0321.vl
  • 44. T. Yokotani and Y. Sasaki, “Comparison with HTTP and MQTT on required network resources for IoT,” in Proc. Int. Conf. Control Electron., Renew. Energy> Commun.,
  • 2016, pp. 1-6.
 
<<   CONTENTS   >>

Related topics