Thursday, September 3, 2020

IoT Based Health Monitoring System

 IoT Based Patient Health Monitoring



With the vastly increasing human populations and medical expenditure, healthcare has become one of most significant issues for both individuals and governments of Bangladesh. Meanwhile, according to a report from the World Health Organization (WHO), the problem of population aging is becoming more serious in Bangladesh. Health conditions of aged people usually need to be checked more frequently, which poses a greater challenge to existing medical systems. Therefore, how to identify human diseases in a timely and accurate manner with low costs has been paid an increasing attention .Due to the dominance in the diagnosis of heart-related diseases, electrocardiogram (ECG) monitoring has been widely applied in both hospitals and medical research .Traditionally, the ECG is detected through large and stationary equipment in professional medical institutions. The kind of equipment usually employs 12 lead using 10 electrodes to collect ECG data due to their good performance in short-term measuring.










































However, the equipment is unlikely to be portable, which means that patient’s activities are severely limited during the period of data collection. Moreover, as these devices are usually too expensive for home use, patients have to go to hospital frequently, which will inevitably increase the burden of hospitals. Therefore, our paper used for a long-term ECG signal detection with low costs is highly desired. Thanks to the development of mobile Internet and wireless sensor networks (WSNs), wearable ECG monitoring systems have emerged which are able to detect ECG signals using a non-intrusive sensor and transmit the signal to the smart phone through wireless transmission techniques, such as Wi-Fi. For the sake of portability, electrodes of the WSN-based ECG monitoring systems are usually less than traditional methods. At the expense of accuracy, it is sufficient to collect the basic information of the heart. These portable sensors are usually embedded into some wearable textiles, which have little impact on the user’s daily activities. With the aid of these systems, long-term ECG can be monitored in a cost-effective manner. However, to the best of our knowledge, nearly all existing systems cannot work without a smart phone, which is used as a receiver and processor of the ECG data. Due to limited power and computational capabilities, the complex tasks of data transportation and processing may have a great impact on the daily use of the smart phone. Furthermore, in order to support all the OS platforms of smart terminals, great efforts are required for the cross-platform development of the mobile application. In this paper, a health care monitoring system based on the Internet-of-Things (IoT) cloud is firstly proposed. Based on this architecture, we design and implement a wearable ECG monitoring system. The ECG data gathered from the human body will be transmitted directly to the IoT cloud using Wi-Fi without the need of a mobile terminal. Compared with Bluetooth or ZigBee, Wi-Fi can provide higher data rates and wider coverage areas. In order to provide convenient and timely access to ECG data for users, both the HTTP and MQTT servers are deployed in the IoT cloud. The gathered data are stored in a non-relational database, i.e., Redis, which can greatly improve the speed and flexibility of data storage. A web-based graphical user interface is implemented so that it provides ease of access for doctors and patient’s alike using smart phones of different OS platforms to access to the data services provided by the IoT cloud. The proposed system has been successfully deployed and fully tested with demonstrated effectiveness and reliability in ECG monitoring. The reminder of this paper is organized as follows. Section “Architecture of IoT Based Health Care Monitoring System” presents the architecture of the IoT Based Health Care Monitoring System. The system implementation is introduced in Section “Implementation of the IoT Based Health Care Monitoring System”, which includes a monitoring node, the IoT cloud and a graphic user interface (GUI). In Section “Experimental Results and Analysis”, we conduct several tests on a healthy volunteer in order to verify the reliability of the proposed system. Finally, Section “Conclusion” concludes this paper.[7]



1.1 IoT Cloud

Thanks to the development of the advanced IoT techniques, ECG data can be stored and analyzed effectively and efficiently. With the aid of an IoT cloud, computation-intensive data process and analysis tasks can be carried out in powerful servers, which greatly eases the burden of smart devices. Generally speaking, an IoT cloud for Heart Beat monitoring and ECG monitoring usually consists of four functional modules, i.e., data cleaning, data storage, data analysis, and disease warning.

v  Data cleaning: Significant features can be extracted from Heart Beat and ECG signals so as to detect potential heart diseases. However, during the processes of data collection and transmission, noise may be introduced into the Heart Beat and ECG signals, which would adversely affect the diagnosis accuracy. Therefore, the signals are need to be cleaned at first. Commonly, a properly designed filter is employed to remove the noise outside the band of the ECG signal. Furthermore, the procedure of data auditing is usually employed to detect data anomalies and contradictions;


v  Data storage: Heart Beat and ECG data plays a vital role in the diagnosis of heart diseases. Thus, historical data are needed to be stored in the database for further analysis. This data often include the time and digitized signal amplitude. In addition, at least one copy of the data needs to be stored for disaster recovery;

v  Data analysis: Making the full use of data is one of the most important functions of the IoT cloud. Therefore, the IoT cloud often provides a data analysis platform to extract useful information from the Heart Beat and ECG signals. Specific data mining or machine learning approaches can be applied to these data. For example, after extracting the significant features of the Heart Beat and ECG signals, a support vector machine can be established to diagnose certain heart diseases; and


v  Disease alert: Sudden heart attacks seriously threaten the lives of cardiac patients, especially when patients are alone. Therefore, disease warning on the IoT cloud has become important for protecting patients from being injured. Based on the results of data analysis, the IoT cloud is able to understand the real-time health conditions of the patient. In the event of any suspicious readings, the IoT cloud will notify the family of the patient and the doctor in time.[5]


1.3 ECG (Electrocardiograph)


ECG or Electrocardiography is a system which can record and measure the electrical activity of the heart over a period of time using electrodes on the skin. Bio monitoring electrodes have passed through a great evolution and progress from 19th century. In 1883, Carlo Matteucci who was a professor of physics at the University of Pisa, first time showed and proposed sensors that watch and monitor the electricity in human body periodically. In 1887, Augustus D.Waller was presented and published the first human electrocardiogram. He was British physiologist. In 1901, Willem Einthoven made re infrastructure of Waller’s technology. Here, he used fine quartz coated with silver in a device which is called the string galvanometer. Einthoven won noble prize for formulate and create the electrocardiograph. In present time, bio monitoring electrodes use in ECG which is made of a plastic substrate covered with a silver chloride ionic compound. The Ag/Acl electrode is mostly used for all the application in bio medical electrode system. These electrodes create an electrical potential and ionic activity in living cells. After connecting the human body, these potentials are demonstrate on the body surface.[1, 3]


The heart starts activation at sinoatrial node which is build and produces heart frequency about 70 cycles per minute. This activation generated to the right and left muscle tissues. There is delay which use to allow the ventricles to fill with blood from atrial contraction in the ventricular node.


These activities help to pump blood to the aorta and to the rest of the body. At last, the re polarization happen and the cycle is repeated time after time. When the cycle take place, the trans membrane potential which measure the voltage difference between the internal and external spaces of the cell membrane create a changes at the each stages. Voltages differences are measured by using the surface electrodes. These different peaks P, Q, R, S, T and U are detect in these stages. in Figure 2.3 it’s shown. [20]

Figure 1.3 Normal sinus rhythm ECG





v  P wave

In the normal heart, each beat begins with the discharge (depolarization) of the sinoatrial (SA) node, high up in the right atrium. This spontaneous event usually occurs 60-100 times per minute. The first distinguishable wave is visible when the impulse spreads from the SA node to depolarize the atria. This is how the P wave is produced. The duration of P wave is 0.06s to 0.12s.

v  PR Interval

The time it takes for the depolarization wave to pass from its origin in the SA node, across the atria, and through the AV node into ventricular muscle is called the PR interval. This is measured from the starting point of the P wave to the starting point of the R wave. The duration of the PR interval ranges between 0.12s to 0.20s.


v  QRS Complex

The QRS complex signifies the expeditious depolarization of the right and left ventricles. As the ventricles have a large muscle mass than the atria, the QRS complex commonly has a much higher amplitude than the P-wave. The duration of the whole QRS complex is 0.06s to 0.10s.


v  ST segment

ST segment represents the period when the ventricles are depolarized. No more electrical current can be passed through the myocardium in this transient period. It starts from the end of the S wave to the starting point of the T wave. ST segment ranges between 0.08s to 0.12s.  


v  T wave

Repolarization (recharging) of the ventricular myocardium to its resting electrical state is called T wave. The duration of a T wave is 0.01s to 0.25s.


v  QT Interval

The time required for the ventricles to activate and readjust to the normal resting state is called the QT interval. It is measured from the starting point of the QRS complex to the end of the T wave. The duration of a QT interval is 0.36s to 0.44s.


v  U wave

Although the origin of the U wave is unresolved, it may represent repolarization of the interventricular septum or slow repolarization of the ventricles.







1.5 Detection of heart abnormalities


Any changes in ECG parameters from their normal values reflect cardiac disorders. For example, any elongation in PQ segment and QT interval indicate heart block and congenital disorders. After the extraction of ECG parameters, this system compares the values with the predefined normal values and indicates corresponding diseases (if any) from the current ECG. Table 2 shows the normal values of ECG and Table 3 shows the abnormal ECG values with associate diseases. Primarily, heart diseases that the proposed system can detect are enlisted below with the detection technique and the enlisted diseases waveform.[18]


1) Tachycardia: Resting heart rate exceeds more than 100 bpm. But the upper limit is 150 bpm. From R-R interval the heart rate (HR) can be calculated to detect tachycardia.

2) Bradycardia: HR falls less than 60 bpm and can be detected as mentioned above.

3) Hypercalcemia: QTc interval time is less than 0.32 sec

4) Hypocalcemia: QTc interval time is greater than 0.44 sec

5) Atrioventricular, block: PR interval is greater than 0.20 sec



Threshold values

ECG parameter







25% of R wave



Normal ECG parameters