A new type of bionics based piezoelectric heartbeat sensor used in pulse-taking for health warning

Meining Ji1, Xiaofeng Meng1, Jing Nie2, *, Yaqin Wang3, Liwei Lin2 1 School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China

2 Mechanical Engineering Department, University of California, Berkeley, 94720, USA 3 Luoyang Dongfang Hospital, Luoyang, 471003, China

* Email: jingnie@berkeley.edu

Abstract—This work demonstrates a novel bionics-based heartbeat sensor combined with approximate entropy (ApEn) algorithm for health warning. The design of the proposed sensor was inspired by pulse-taking therapy in traditional Chinese medicine (TCM). A flexible piezoelectric film was adhered to a wooden cylinder to simulate the structure of the finger to achieve pulse-taking. The simulation of piezoelectric film under different force area and bending degree was realized by ANSYS. The results showed that the sensor structure proposed in this paper can not only simulate the characteristics of the finger to achieve the TCM pulse-taking, but also improve the sensitivity of piezoelectric film. The sensor was used to measure the pulse signal of human under different states of motion and different health conditions. Then the sample data were analyzed using the ApEn. The results showed that the ApEn value is 0.1 can be used as a threshold for judging and predicting human health status. The experiment proved that this method can avoid the requirement of the same force in the traditional pulse-taking. It not only can obtain the low distortion pulse signal, but also can obtain the heart rate accurately. Using this method can quickly achieve rapid detection and early warning of human health.

Keywords—traditional Chinese medicine; heartbeat; pulse; piezoelectric sensor; approximate entropy

I. INTRODUCTION

With the contraction and relaxation of the heart, the fluctuations of the blood vessels can be sensed at the superficial part of the arterial blood vessel, and the period is equivalent to the beat period of the heart, which is called the pulse. The pulse is driven by the heart and spreads along the artery to the wrist. It contains a lot of human physiological health information such as changes in blood pressure, degree of arteriosclerosis and rhythm of the heartbeat, etc [1-2]. Pulse-taking is a unique diagnostic method of traditional Chinese medicine (TCM). It detects the health status of a patient by analyzing the pulse manifestation. Sometimes the pulse manifestation has already changed before the symptoms appear completely, so the pulse-taking diagnosis of TCM has important clinical significance in analyzing the etiology and inferring the changes of the diseases.

In this work, we proposed a new type of heartbeat sensor based on finger bionics design. Combine the piezoelectric film with the wooden cylinder to simulate the pulse-taking diagnosis process of TCM. The finite element simulation

method was used to verify the superiority of using proposed sensor structure to measure pulse signals. Then, the ApEn algorithm was applied to detect the different motion states and different health conditions of the human body, and a rapid health warning method was successfully obtained.

II. DESIGN AND OPTIMIZATION OF SENSOR STRUCTURE

A. Sensitive Mechanism and Design of the Sensor Fig. 1(a)-(b) shows the bionic principle and configuration

of the proposed heartbeat sensor, which is composed of three components: a wooden cylinder, a piezoelectric film, and two gold electrodes are plated on both sides of the film. The flexible piezoelectric film is closely adhered to the wooden cylinder to simulate the physiological structure of the finger and sensitive mechanism of finger belly during the pulse-taking. The film is cellular piezoelectric electret film, which has the advantages of good flexibility, light weight [3]. Wood material is selected as the curved base, not only its insulating properties, but also because it has the advantages of moderate hardness, light weight, and easy processing. The pulse-taking process of this sensor shown in Fig. 1(c). In the diagnosis of the pulse, the appropriate strength is conducive to finding and sensing pulse beats. Therefore, a medical clip is used to fix the sensor to the wrist, which can be adjusted by three force levels, so that the tightening force can be randomly adjusted according to the wrist size of different people.

Fig. 1. Design principle of sensor. (a) Proposed heartbeat sensor structure based on bionics. (b) Configuration of the proposed heartbeat sensor. (c) Heartbeat sensor used in pulse-taking.

B. Optimization of the Structural Parameters The authors acknowledge support from the National Natural ScienceFoundation of China (61603349) and (61473021), the National NaturalScience Foundation Innovation Group of China (61421063).

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978-1-5386-4707-3/18/$31.00 ©2018 IEEE

In the traditional pulse measurement, a piezoelectric film is applied directly to the skin, and a fixed pressure is applied to enhance the sensitivity. However, the diameter of the human radial artery is about 3-5 mm, so when the contact area between the film and the skin is too large by using the traditional method, the actual piezoelectric sensing efficiency is reduced. In this study, the sensor structure was designed based on the characteristics of the finger, so the actual contact area between the film and the skin was small and the film was in a curved state. In order to qualitatively verify whether the piezoelectric conversion efficiency of the piezoelectric film can be improved in this structure state, the working mode of the piezoelectric film was simulated and numerically calculated using ANSYS simulation software, and the optimized parameters of the sensor design were obtained.

The simulation model of the piezoelectric film is a uniform hexahedral structure with (L) mm length, 20 mm width and 0.3 mm thickness, and a contact force with a magnitude of 1 N was applied to the model upper surface, as shown in Fig. 2(a). In the simulation, the length L was increased from 1mm to 20 mm in steps of 1 mm, and the relationship between the force area and the output voltage of the piezoelectric film was studied, the result is shown in Fig. 2(b). It can be seen from the result that the voltage value decreases with the increase of L, which means that the output voltage of the piezoelectric film decreases as the force area increases. It is because that when a certain force is applied evenly to the film surface, the smaller force area of the film, the greater the deformation caused by the pressure. More charges are generated on the film surface due to the great deformation, then the output voltage of the film is increased. According to the width of the radial artery, the area where L is in the range of 3-5 mm is the effective force region of the pulse on the piezoelectric film (red area in Fig. 2(b)). The simulation result shows that the piezoelectric film can generate more charge and have higher sensitivity when the force is constant, and the force area is small.

Fig. 2. Simulation of different force areas. (a) Schematic diagram of simulation model of the piezoelectric film. (b) The relationship between the force area of the piezoelectric film and its output voltage.

Based on the previous simulation, the bending angle of the model was gradually changed, and a force of 1 N was applied to the central region of 4 mm (red region in Fig. 3(a)) on the model upper surface, so that to study the relationship between the bending angle and the output voltage of the piezoelectric film. The bending angle (θ) is described in Fig. 3(a), and the simulation result is shown in Fig. 3(b). The

result shows that the piezoelectric film deforms more in the bending state, so the film will produce more charge and have higher sensitivity in the bending state than the plane state under certain conditions. However, the large θ can make the contact of the film with human skin sharp, which affects the wearing comfort. Therefore, in this study, the choice of θ is 60°, which can not only obtain a relatively large signal but also ensure the comfort of the human body.

Fig. 3. Simulation of different bending degrees. (a) Schematic diagram of film bending change. (b) The relationship between the bending degree and the output voltage of the piezoelectric film.

The relation between θ and bending radius (R) can be described as:

L R⁄ = π 180o⁄ ×4θ (1)

In the experiment, L is 20 mm. When θ is 60°, R is 4.77 mm obtained from equation (1). Therefore, choose a wooden cylinder with a diameter of 9.5 mm to make the sensor. Based on the simulation and analysis results, the sensor structure proposed in this paper can not only simulate the characteristics of the finger to achieve the TCM pulse-taking, but also improve the sensitivity of piezoelectric film.

III. PERFORMANCE OF THE HEARTBEAT SENSOR

Fig. 4 is schematic of the experimental measurement system. The system consists of a heartbeat sensor, a current preamplifier (Stanford Research Systems, SR570), a data acquisition card (NI Usb-6002) and a personal computer (PC). In the experiment, the output signal of the sensor is amplified via the current preamplifier (magnification for 50 pA/V), and then the analog-to-digital conversion of the signal is performed by the data acquisition card. Eventually realize signal display and storage on the computer, for further processing and analysis by MATLAB. The sampling rate in the experiment is 60 Hz.

Fig. 4. Schematic of experiment system set-up

In order to study the proposed sensor performance, we collected 30 seconds pulse signal of a healthy volunteer at calm state, after exercise, and one hour of rest after exercise, respectively. From the results shown in Fig. 5(a)-(c), we can clearly see the waveform characteristics of the pulse and the change of heart rate before and after exercise, which shows that the proposed sensor has low distortion and good repeatability when the pulse signal is acquired under any

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motion state of the human body. Approximate entropy (ApEn) is a method to represent the complexity of nonlinear time series [4]. It carries out the phase space reconstruction of m and m+1 dimensions respectively for the time series, thus calculating the probability that the time series will generate new mode when the dimension increases from m to m+1. It requires only a short data to get a robust estimate, so it is particularly useful for non-stationary biological signals. Based on the above experiment, each 360 points was used for ApEn calculation and five values of ApEn were acquired in each motion state. As descried in Fig. 5(d), the five ApEn values of pulse signal after strenuous exercise were all less than 0.1, and the ApEn values before exercise were almost the same as one hour after exercise. The cause of this phenomenon is approximately that the exercise will increase the load of the heart and thus affect the inherent complexity of the pulse signal. It can be judged qualitatively that the characteristics and complexity of human pulse signal will be greatly changed after being physically form stimulated.

Fig. 5. Time - domain signals under different heartbeat states: (a) Before exercise (b) Exercise and (c) Recovery. (d) Approximate entropy values in different heartbeat states.

As shown in Fig. 6, we further calculated the average of the five ApEn values of 8 volunteers’ (4 male and 4 female) pulse data so that more accurately verify the ApEn value can qualitatively describe the human health status and studied the effect of the force on the algorithm results.

By asking and view volunteer health report, we know that one person suffering from arrhythmia and another two were accompanied by influenza and insomnia when measuring. These conditions all affect the normal work of the heart, and then affect the complexity of the pulse signal. The actual situation is consistent with the results determined by ApEn value, which further validates our previous conclusions, and can set 0.1 as the threshold for judging health condition. From the results of different force, we know that the magnitude of force has almost no change on the algorithm results. The result proves that using the ApEn algorithm to analyze the pulse signal, the force acting on the skin has little effect on the composition and complexity of the signal. The detailed experimental results are list in Table 1. It can be seen that we proposed sensor can accurately obtain the

heart rate, and the heart rate is not directly related to the ApEn, which more show the feasibility of using ApEn for health warning.

Fig. 6. Approximate entropy of (a) pulse signal in 8 volunteers and (b) signals under different force.

TABLE I. DETALIED EXPERIMENTAL RESULTS

IV. CONCLUSION

In this work, we proposed a new bionics-based heartbeat sensor using flexible piezoelectric film and combined with approximate entropy (ApEn) algorithm for health detection and warning. The pulse waveform of low distortion and accurate heart rate were obtained. Through the ANSYS simulation of the piezoelectric film, we learned that the proposed sensor structure can not only simulate the characteristics of the finger to achieve the TCM pulse-taking, but also improve the sensitivity of the piezoelectric film. Then, we analyzed the pulse signals in different motion states, different health conditions and different tightening forces with ApEn algorithm. The results show that the ApEn value is 0.1 can be used as a threshold of judge and predict the health status, and this method can avoid the requirement of the same force in the traditional diagnosis.

ACKNOWLEDGMENT

Authors would like to thank Dr. Junwen Zhong at Berkeley Sensor and Actuator Center for help with fabrication of piezoelectric electret film. We are grateful to the volunteers and the Luoyang Dongfang Hospital in China.

Sample number

ApEn Heart rate by

our sensor (30 s)

Heart rate by health report

(30 s)

Health condition

1 0.237 45 45 Health 2 0.229 40 40 Health 3 0.272 39 40 Health 4 0.185 32 32 Health 5 0.213 36 38 Health 6 0.037 56 55 Arrhythmia 7 0.048 36 36 Influenza 8 0.028 48 51 Insomnia

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