Design and Implementation of Portable and Compact ...

Design and Implementation of Portable and Compact Human Heartbeat Rate Monitoring System Kishor Kumar Das(&), Ram Kishore Roy, Hidam Kumarjit Singh, and Tulshi Bezboruah Department of Electronics and Communication Technology, Gauhati University, Guwahati, India {kkdas0112, r.kore51guece}@gmail.com, [email protected], [email protected]

1 Introduction In our body, heart is responsible for providing blood circulation continuously. A cardiac cycle consists of one contraction (systole) and one relaxation (diastole) state of heart. During systole state, the heart collects blood and at the end of diastole state, the purified blood pumps out to the blood vessels. Contraction of atria starts the cardiac cycle and the relaxation of ventricles completes the cardiac cycle of heart. One heartbeat is one complete cardiac cycle. Heartbeat rate (HBR) can be defined as the cardiac cycle per minute or beats per minute (bpm). Adult person can have average HBR of 72 bpm. Normal HBR for resting adult person is in the range of 60–100 bpm. If the HBR falls below 60 bpm, it is called Bradycardia, and if it is faster than normal range, i.e., 100 bpm, then it is called Tachycardia. The HBR changes during sleep, stress, anxiety, illness, physical activities, and cardiovascular disease. There are two techniques for HBR measurement, namely: (i) electrocardiography (ECG) and (ii) photoplethysomography (PPG) [1]. During each heartbeat, some electrical changes occur in our body which is detectable by placing suitable sensors over some of our body part. ECG is used to detect this changes that uses electrodes on the skin over our chest. This method requires direct electrical connection with our body through the electrodes. It is a commonly used method for medical purpose and requires medical experts for operating the device. The PPG works on the optical properties. It is noninvasive method which does not require direct electrical connection. It is a technique which is based on the light reflection and transmission properties. It uses light source and light detector. The PPG can measure blood volume changes in an organ, e.g., fingertip. In a cardiac cycle, the heart contracts and expands, which causes a change in blood volume in the finger. Blood volume inside the fingertip increases during the diastole state and decreases during the systole state of a cardiac cycle. Blood volume synchronously changes with the heartbeat. The pulse of blood volume inside the fingertip is directly proportional to the HBR. We can determine the HBR in bpm by counting the number of pulses per minute. The PPG signals can be obtained in two modes, namely: (a) light reflection mode (LRM) and (b) light transmission mode © Springer Nature Singapore Pte Ltd. 2018 S. Bhattacharyya et al. (eds.), Advanced Computational and Communication Paradigms, Lecture Notes in Electrical Engineering 475, https://doi.org/10.1007/978-981-10-8240-5_26

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(LTM). In LRM, the medium is placed over the sensor system or vice versa. A photodetector (PD) detects the reflected light from the medium. The medium may be fingertip, forehead, etc. In LTM, the medium is placed between the light source and the PD. A PD detects the transmitted light in the medium. The medium can be fingertip, earlobe, etc. [1]. The PPG signal can be obtained by using simple electronic circuit. Though PPG has many advantages, it has some limitations too. They are as follows: (i) it has large DC component compared to the actual PPG signal, (ii) the PPG signal is undetectable for reduced blood level, (iii) other sources of light affects the PD, and (iv) the reflected light received by the PD changes due to motion.

2 Related Works In the year 1991, Y. Iyriboz et al. had studied the noninvasive measurement of heart rate accurately at rest and during exercise. But during heavy exercise, this method shows some error about 9% [2]. In the year 1992, Joseph M. Schmitt et al. had proposed a noninvasive method that can measure hemoglobin concentration and oxygen saturation continuously [3]. In the year 2008, Fezari et al. had developed a real-time heart rate monitoring system by using microcontroller (µC). Doctor can access patient’s pulse rate file sent through email every 24 h. Athletic persons can use this system which can read, write, and analyze the pulse rate [4]. In the year 2009, Jubadi et al. had proposed a heart rate monitoring system which can send the resultant HBR as SMS to a family member or to doctor. This is based on the PPG technique which is explained above. The sensor output was processed by using PIC16F87 µC to count the HBR per minute. An alert was given to medical experts or family members via SMS. With the help of this system, doctors could monitor and diagnose patient’s condition continuously and could suggest them precautions if any [5]. In the year 2012, Verma et al. had developed a system by using which patients are able to measure their own heart rate and body temperature. Health professionals can examine the status of the patient through messages sent by GSM module connected to the system along with time, date, name of the patient, location of the patient, etc. [6]. In the year 2012, D.J.R. Kiran Kumar et al. had developed a health monitoring system and necessary data acquisition system to study remotely the parameters of health of the patient like heart rate, ECG, body temperature, blood oxygen saturation level, and blood pH level. Using this system, a doctor can monitor a patient on PC using Zigbee wireless module [7]. In the year 2012, Chi Kin Lao et al. had proposed a portable pulse rate detector system. In this system, the heart rate was calculated from the PPG signal and transmitted to computer or smartphone by using radio frequency (RF) transmitter section. To view the HBR on smartphone, they have also developed an Android-based application [8]. In the year 2013, Deshmukh had developed a real-time patient monitoring system which helps medical professionals to see status of patient’s health through website.

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This system has temperature sensor and pulse rate sensor along with a Wi-Fi module [9]. In the year 2013, N. Singh et al. had designed sensor nodes for measuring heart rate and temperature, which have remote monitoring capability along with wireless sensor module. This system can transmit the sensor data wirelessly to the remote monitoring station and to the controller by using RF transmitter and receiver module [10]. In the year 2013, Venugopal et al. had developed a centralized heart rate monitoring system. Here, the sensor data is collected from various patients and transmitted over a Wireless Local Area Network (WLAN). This system can be used in medical purposes [11]. In the year 2014, S. Saravanan had proposed a system that can monitor heartbeat of a patient and capable to send the data by using computer communication networks, which support Wi-Fi, Bluetooth, etc. This system can monitor ECG, pulse, blood pressure, arterial oxygen saturation, blood glucose concentration, etc. They constructed a peer-to-peer messaging system which can send to the doctor where the monitoring section receives the data via Wi-Fi, Bluetooth, and Internet [12]. In the year 2014, Purnima et al. had proposed a GSM and Zigbee technology-based health monitoring systems which can monitor and transmit ECG, temperature, and heartbeat signals continuously. Every patient monitoring system was connected to a Zigbee node. Doctors can receive the signals through computer or mobile via Zigbee as well as GSM technology [13]. In the year 2014, Chandana et al. had proposed a system by using which one can stay connected with doctor. In emergency, one can take immediate action by using this system. This system can measure blood pressure, drug level, and HBR. This data can be sent via GSM module to the doctor’s mobile [14]. In the year 2014, M.V.N.R. Pavan Kumar et al. had developed a GPS-based system for health monitoring and tracking, which focuses on tracking a soldier’s location, so that control stations can guide them in emergency. Using this system, central base station can get information about the body temperature, heart rate, and the location of a soldier [15]. In the year 2014, S. Bae et al. had proposed a health management system based on self-organizing software platform that uses smart devices. One can add new devices and services in this plug-in system without modification. They proposed a smart watch that can measure blood pressure of the person who is wearing the watch. The blood pressure monitor identifies the smart watch by transmitting a low-frequency signal. In order to record the blood pressure, the data collected by the blood pressure monitor is transmitted to the watch. Then, the watch sends this data to the router and to the main server [16].

3 Proposed Methodology The objective of the proposed work is to design, fabricate, and implement an HBR monitoring system. Here, we have used one infrared (IR) light-emitting diode (LED) (wavelength: 930 nm) as light source and one IR detector in light reflection


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mode. While we place our index finger over the IR sensor, the IR LED emits light which can pass through our finger. A portion of light is reflected back. The intensity of reflected light depends on the blood volume inside the fingertip. The intensity of reflected IR wave slightly alters with each heartbeat in a harmony. The IR detector detects this change of reflected IR signal. The pulsating reflected signal detected by the IR detector is converted to current or voltage signal by using the sensor circuit. The sensor output is visualized in digital storage oscilloscope (DSO: Model: Tektronix, TD20014C) and is processed by using µC, and the recorded data is then transferred to PC for storage. The proposed block diagram of the system is shown in Fig. 1.

Fig. 1. Block diagram of the system

4 Circuit Implementation 4.1

The Sensor Circuit

An optical sensor TCRT5000 (from Vishay Semiconductors) is used to obtain the PPG signal in reflection mode. It consists of an IR emitter and a phototransistor. It can also block visible light. The sensor system is connected to +5 V power source as shown in Fig. 2. The signal detected by the sensor is fed to the amplifier circuit by using a coupling capacitor. 4.2

The Filter and Amplifier Circuit

The heartbeat signal frequencies lie between 0.5 and 4 Hz. We have used a low pass filter to bypass the high-frequency components above the required frequency level. The sensor output is fed to the non-inverting terminal of the first amplifier through a coupling capacitor. The capacitor C1 and the resistor R3 form a low-pass filter. The cutoff frequency of the filter is calculated as Fs ¼

1 2pR3 C1

ð1Þ

Here, the cutoff frequency is almost 2.4 Hz. An amplifier is used to amplify the amplitude of the PPG signal. We have used IC 741 operational amplifier (OPAMP) based amplifier in non-inverting mode. The gain for the first stage of the amplifier is fixed at 101. We used a two-stage amplifier circuit for the system, and the overall gain of amplifier is *104. The amplified output from the first stage is fed to the second stage of the non-inverting amplifier as shown in Fig. 2. The cutoff frequency and the

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gain are same for the second stage filter and amplifier as in the first stage. The final amplified output is fed to a comparator. 4.3

The Comparator Circuit

The Comparator gives a binary output signal indicating which is larger by comparing two voltages or currents. It has two analog input terminals VIN and VREF. The output of a comparator can be formulated as VO ¼

1; VIN [ VREF 0; VIN VREF

ð2Þ

Here, we have used OPAMP to design a comparator circuit as shown in Fig. 2. One adjustable reference voltage is applied to the non-inverting terminal. We have used one Trimpot (Bourns 3296) to adjust the reference voltage. The amplified sensor signal is applied to the inverting terminal. The resistance, R9, and capacitor, C5, are used to remove unwanted noise components from the input signal, which also increases the stability of the signal. The comparator gives logic level output by comparing the input signal with the applied reference voltage. The comparator output is fed to µC 8051 for further processing and display.

Fig. 2. Signal conditioning circuit diagram

4.4

Interfacing and Processing

The complete setup of the system has been shown in the Fig. 3. The comparator output is directly interfaced to Timer 1 (pin 15) of 8051 µC. We have used timer 0 for 1 min delay generation and Timer 1 for counting the pulse rate from the PPG signal, since the HBR is measured as the number of heartbeats in 1 min. We have created one window of 1 min duration to calculate HBR that occurs during this interval. The timer and counter start from 0 and count the pulses and stop after 1 min duration. The data are


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stored in memory location. The count value gives the HBR per minute. To display the result in LCD, the resultant data which are in Hexadecimal code are converted to Decimal and then the Decimal to ASCII code. Then, the data are sent to the LCD display JHD162A through Port 1 (pin 1–in 8) of 8051 µC continuously. The DSO is interfaced to the sensor circuit for capturing the output data. The sample output PPG signal captured by DSO is shown in Fig. 5a. Also, an algorithm has been developed for monitoring the HBR at the interval of 5 s.

Fig. 3. Snapshot of the experimental setup

The theoretical HBR is calculated by using the period from peak to peak of the PPG signal. Reverse of the time period gives the frequency (f). Then, the HBR can be calculated as HBR ¼ 60 f bpm

ð3Þ

5 System Calibration The system has been calibrated and tested by using a test signal from function generator. Signals with different frequencies have been applied to the signal conditioner circuit, and the data are displayed in LCD after processing through the DAQ system. The plot of standard and measured HBR at different frequencies of the input signal is shown in Fig. 4a. The plot of % error between standard and measured HBR versus frequency of the input signal is shown in Fig. 4b. Now, we have applied the PPG signal to the input of the system which can process the PPG signal and give HBR per minute.

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Fig. 4. a HBR standard and measured versus input frequency curve, and b % error versus frequency curve

6 Result and Discussion The PPG signal output and the comparator output are shown in Fig. 5a. Theoretically, the HBR is calculated by using Eq. (3). In our experiment, the time period for the PPG signal is 800 ms, and the frequency is 1.25 Hz. Therefore, HBR ¼ 60 f bpm ¼ 60 1:25 bpm ¼ 75 bpm which is equal to the value calculated by using the algorithm, and the HBR is displayed in the LCD screen as shown in Fig. 5b.

Fig. 5. a Snapshot of sensor output versus comparator, and b snapshot of HBR displayed in


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The recorded HBR for different persons is given in Table 1. Table 1. Experimental and theoretical values of HBR Sl. No. 1 2 3 4

Person A B C D

Theoretical HBR (bpm) 76 62 62 70

Practical HBR (bpm) 75 65 64 68

(%) Error 1.31 4.84 3.23 2.86

7 Conclusion In the proposed work, we have developed an HBR monitoring system. The system is tested and found that it is capable of monitoring the HBR of patient and other persons at home or during indoor activities. The system is noninvasive, because it is desired to be used during outdoor exercise or other physical activities. The system is desired to be run by the body power generated using piezoelectric sensor. Increasing movement of person leads to decrement of the accuracy of HBR measurement. A proper filter should be incorporated into the system to remove the unwanted signal components, which affect the PPG signal during motion. Acknowledgements. The authors are thankful to Assam Science Technology and Environment Council (ASTEC), Govt. of Assam, India for providing financial support towards the work (Grant no. ASTEC/S&T/192(152)/14-15/1037 dated 19.05.2015).

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9. Deshmukh RS (2013) Wi-Fi based vital signs monitoring and tracking system for medical parameters. Int J Eng Trends Technol (IJETT) 4(5):1935–1938 10. Singh N, Mishra R (2013) Microcontroller based wireless temperature and heart beat read-out. IOSR J Eng (IOSRJEN), 01–06 11. Venugopal K, Kumar A (2013) Centralized heart rate monitoring and automated message alert system using WBAN. Int J Sci Res Publicat 3(9) 12. Saravanan S (2014) Remote patient monitoring in telemedicine using computer communication network through bluetooth, Wi-Fi, internet android mobile. Int J Advanc Res Comput Commun Eng 3(7):7590–7596 13. Purnima P, Singh P (2014) Zigbee and GSM based patient health monitoring system. Int Confer Electron Commun Syst (IECS) 14. Chandana D, Hema Latha B (2014) A tele-medicine system for measuring heart rate, blood pressure, and drug level detection. IJEDR 2(1):23–29 15. Pavan Kumar MVNR, Vijay GR, Adhikrao PV, Vijaykumar BS (2104) Health monitoring and tracking of soldier using GPS. Int J Res Adv Technol 2(4) 16. Bae S, Kim K, Lee DY (2014) A scalable health management system based on a self-organizing software platform. IEEE, 70–80