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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].


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.


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.


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.


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.


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


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