Human Pulse Diagnosis for Medical Assessments Using a

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Human Pulse Diagnosis for Medical Assessments Using a Wearable Piezoelectret Sensing System

Yao Chu, Junwen Zhong,* Huiliang Liu, Yuan Ma, Nathaniel Liu, Yu Song, Jiaming Liang, Zhichun Shao, Yu Sun, Ying Dong,* Xiaohao Wang, and Liwei Lin*

Real-time and continuous monitoring of physiological signals is essential for mobile health, which is becoming a popular tool for efficient and convenient medical services. Here, an active pulse sensing system that can detect the weak vibration patterns of the human radial artery is constructed with a sand-wich-structure piezoelectret that has high equivalent piezoelectricity. The high precision and stability of the system result in possible medical assessment applications, including the capability to identify common heart problems (such as arrhythmia); the feasibility to conduct pulse palpation measure-ments similar to well-trained doctors in Traditional Chinese Medicine; and the possibility to measure and read blood pressure.

DOI: 10.1002/adfm.201803413

Y. Chu, H. Liu, J. Liang, Prof. X. Wang, Prof. L. LinSensors and Microsystems LaboratoryTsinghua-Berkeley Shenzhen InstituteTsinghua UniversityShenzhen 518055, ChinaE-mail: [email protected]. Chu, Dr. J. Zhong, H. Liu, Dr. Y. Ma, N. Liu, Dr. Y. Song, J. Liang, Z. Shao, Prof. L. LinBerkeley Sensor and Actuator Center and Department of Mechanical EngineeringUniversity of CaliforniaBerkeley, CA 94720, USAE-mail: [email protected]. Sun, Prof. Y. Dong, Prof. X. WangGraduate School at ShenzhenTsinghua UniversityShenzhen 518055, ChinaE-mail: [email protected]

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adfm.201803413.

imitate the TCM practice for health assess-ments without well-trained, real doctors. Previously, human pulse waves have been measured using sensors based on different detection mechanisms, such as optics,[16,17] image processing,[15,18,19] acoustics,[20] and pressure means,[21–29] etc. Among these, active pressure sensors based on the working principles of piezoelectricity[21–24] or triboelectricity[7,8] are the more intuitive and sensitive method to detect pulse waves, as these sensors can accurately and directly reflect the weak vibration of the radial artery to better imitate the pulse diagnosis in TCM. Several vertical contact-separation

and single-electrode triboelectric pressure sensors have been pub-lished to detect human motion and physiological signals, with advantages of thin, flexible, and excellent sensitivity.[30] However, vertical contact-separation devices are composed of two separated parts, which increases the difficulty of assembly and operation in practice. Single-electrode triboelectric sensors have been pro-posed to address this issue but the exposed residual charges on the sensor surface can be easily leaked. Piezoelectret is flexible, lightweight, and have large and stable equivalent piezoelectric coefficient for high sensitivity.[25] Furthermore, sensors based on piezoelectret materials can alleviate the aforementioned issues in triboelectric sensors to detect physiological signals.

In this work, we use an active and flexible pulse wave sensing system based on a fluorinated ethylene propylene (FEP)/Eco-flex/FEP sandwich-structured piezoelectret for piezoelectric-like detections.[25,26,31] Several key features are accomplished from the prototype sensing systems: 1) excellent precision and stability capable of differentiating and classifying pulses from different volunteers coupled with the help of big data analyses; 2) the identification of a common heart problem (arrhythmia) from volunteers who were previously diagnosed in hospitals equipped with advanced and bulky electrocardiogram (ECG) setups; 3) the feasibility in recording and revealing the blood pressure using the pulse sensing system instead of a common blood pressure gauge; and 4) the imitation and recording of 3-finger pulse palpation sig-nals commonly used by doctors practicing in TCM.

2. Results and Discussion

The sensing device together with the wireless transmission and big data analysis functions can act as a wearable m-Health system (Figure 1a). In this case, three pulse sensors are illus-trated at the Cun, Guan, and Chi pulse locations following the

Health Monitoring

1. Introduction

The rapid progresses of portable and wearable electronics have greatly advanced the mobile health (m-Health) technologies in recent years to assist people’s daily life.[1–5] For example, 83% of physicians in the U.S. have already used various m-Health tech-niques to provide patient cares,[6] in which different systems are applied to monitor a variety of physiological signals, such as pulse wave, blood pressure, and body temperature.[1,7–14] Among these, human pulse wave has been utilized for thousands of years in Tra-ditional Chinese Medicine (TCM) to predict and prevent diseases at the early stages.[7,8,15] Today, the innovations in sensing technol-ogies and utilizations of artificial intelligence have become strong foundations for the possible diagnostics of human pulses to

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TCM practice. The blood artery at these locations experiences wave fluctuation due to the cardiac ejection of blood to cause different patterns corresponding to the blood volume, pressure, and flow. After years of experiences, TCM doctors can make medical assessments by using their fingers to apply different pressures and sense the pulse signals without instruments or quantitative analyses. In order to collect large amounts of pulse data for big data analyses and diagnoses, one key challenge is to reliably and accurately measure the pulse signals using a pulse sensing system.

An FEP/Ecoflex/FEP sandwich structure is used for the pie-zoelectret system (Figure 1b) with detailed fabrication steps in Figure S1 in the Supporting Information. The scanning elec-tron microscope (SEM) image (inset in Figure 1b) shows that

two FEP films and one Ecoflex film at the middle (with 2 mm circular holes on the Ecoflex film) are bonded together to form the sandwich structure. After a corona charging process (Figure S2, Supporting Information), megascopic electrical dipoles are formed to induce the piezoelectricity-like func-tions.[31,32] In theory, larger circular hole ratio in the Ecoflex spacer is helpful to increase the performance of our piezo-electret pulse sensor, since the electrical dipole density will be improved. The equivalent piezoelectric coefficient d33 of the system (placed in the indoor environment for 10 weeks) is measured by a “weight moving method”[33] and characterized as about 4100 pC/N (Figure 1c), which is much higher than those of the traditional piezoelectric and piezoelectret materials, such as polyvinylidene fluoride (about 33 pC/N),[34] lead zirconate

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Figure 1. Piezoelectret sensing system for pulse waves detection. a) The concept of an m-Health system based on the pulse sensing system to detect the pulse waves at the Cun, Guan, and Chi positions following the practice of TCM for the applications in medical assessments. The signals can be transmitted wirelessly to a mobile device such as a cell phone to the cloud for data analyses. b) The schematic diagram of the pulse sensing device using the FEP/Ecoflex/FEP sandwich-structured piezoelectret film. Inset (upper left corner) shows the cross-sectional SEM image of the sandwich-structured piezoelectret film. c) Equivalent piezoelectric coefficient d33 measurement with a “weight moving method”. d) The working process of a pulse wave sensor during one period of pressing (I–II) and releasing (III–I) to induce electrical currents flowing in opposite directions. e) Peak short-circuit currents and sensitivity of a typical pulse sensor under varying applied pressure from 0.125 to 22.5 kPa under the frequency of 1.5 Hz.



titanate (about 250–700 pC/N),[34] and cellular polypropylene (about 600 pC/N).[33] The high equivalent quasi-static d33 value of our sandwich-structured piezoelectret benefits from abundant electrical dipoles (FEP can maintain rich and stable charges to form the abundant dipoles[35]) and low Young’s mod-ulus (0.1 MPa for the Ecoflex spacer[36]). Finally, a 50-nm-thick gold and 10-µm-thick aluminum electrode are applied on the top and bottom surfaces of the piezoelectret system. The thin gold electrode is chosen to allow the mechanical deformation of the FEP film to be converted to electrical signals.

At the original state (Figure 1d, I), the electrical potentials between the two electrodes are the same without any electrical response. When an external pressure is applied to compress and deform the system (Figure 1d, II), the dipole moment changes to generate a positive current in the external circuit between the two electrodes (Figure S3, I–II, Supporting Infor-mation). If the pressure is removed (Figure 1d, III), the system recovers back due to elasticity to its initial shape and the short-circuit current generates a negative current (Figure S3, III–I, Supporting Information). A setup that can apply controllable mechanical stimulation is used to study the sensitivity of our sensor (Figure S4, Supporting Information). Figure 1e shows the peak short-circuit currents of a typical pulse sensor under varying applied pressure from 0.125 to 22.5 kPa under the fre-quency of 1.5 Hz (close to the heartbeat rate). The sensitivity of the pulse sensor is roughly divided into two regions, with the slope of 32.6 nA kPa−1 between 0.125 and 5.25 kPa and 6.71 nA kPa−1 between 7.50 and 22.5 kPa, respectively. The linear behavior of current versus deformation/strain is similar to other piezoelectric and piezoelectret generators.[21,25,32] Two regions with different sensitivities are observed in our work as Ecoflex is known to have two linear regions in the stress–strain responses[37] (easier to deform under low stress region) to reflect in our device characterizations.

The response time is investigated under a constantly applied force source of 9.75 kPa and frequency of 1.5 Hz (using a res-onator and force meter setup), as shown in Figure S5 in the Supporting Information. The response time of a typical pulse sensor is about 18.6 ms. In terms of durability, the pulse sensor is stimulated under a constantly applied pressure of 9.75 kPa and frequency of 1.5 Hz for over 3600 cycles and the short-circuit current versus time curve is shown in Figure S6 in the Supporting Information to be very stable during this durability test. Moreover, the electrical dipoles are formed inside the air bubble cavities of sandwich-structured piezoelectret and the external surface are covered with metal electrode. As such, our pulse sensor is stable over various humidity conditions. As indicated in Figure S7a in the Supporting Information, a pulse sensor is placed into water vapor and continuously oper-ated under given applied pressure of 9.75 kPa and frequency of 1.5 Hz. The output short-circuit currents do not show decay after continuous operating for 30 min under such extremely wet environment (Figure S7b, Supporting Information).

The pulse wave sensor can be assembled with a wristband to sense weak signals from the radial artery at the wrist (Videos 1 and 2, Supporting Information). The size of the prototype sensor is 6 × 4 cm2 to fully cover the radial artery area on the wrist (about 2.0 × 0.2 cm2).[38] The measured electrical signals from the sensor are preprocessed and sampled by a pre-amplifier

(Figure S8a, Supporting Information) and then transferred wirelessly to a mobile device (Figure S8b, Supporting Infor-mation) and the detailed circuit design is given in Figure S9 in the Supporting Information. The Bode plots of the filter inside the pre-amplifier are shown in Figure S10 in the Sup-porting Information and the filtered responses of square waves with frequency near heart beat (0.5–2.5 Hz) filtered responses are shown in Figure S11 in the Supporting Information. Above results indicate that our pulse wave sensor can acquire the static response to the pressure generated by radial artery.

The stability and precision of the pulse wave sensor are crit-ical for long-term health monitoring applications. Experimental data are collected as shown in Figure 2a with little baseline drift by continuously recording the pulses from a volunteer (a 28-year-old male) for 180 s. It is found that the peak intensity fluctuates within 15% of the maximum value mainly due to the physiological movements of the volunteer. The enlarged figure in the inset shows the detailed waves and the instantaneous heartbeat rate at about 78 bpm (Figure S12, Supporting Infor-mation). The raw signal for one cycle is resampled with 1000 points and the systolic peak (P1), point of inflection (P2), and dicrotic wave (P3) are clearly distinguishable (Figure 2b). The interval period (∆t) between P1 and P2 and the intensity ratio (R2/R1) of the P2 and P1 peaks are key features for pulse diag-nosis.[8] The same 180-s measurement is repeated using the same sensor in a time span of 4 weeks. Figure 2c,d shows that the recorded ∆t and R2/R1 for a 4-week period and the values maintain at around 0.26 s and 30%, respectively, without large variations.

TCM doctors analyze pulse waves at three positions under different pressure levels and characterize them as 28 different types, such as floating, slippery, and thread, etc. It is believed that pulse wave will remain steady for each person in the short term, and if an obvious pattern change of the pulse wave is detected, it implies underlying health issues.[39] Therefore, the first task for the human pulse diagnosis is the reliable differentiation and classification of pulse patterns. Figure 3a illustrates results of classifications of pulse waves via big data analysis based on the machine learning technology. The time-series pulse waves are first truncated into a single period and resampled to 100 points for each cycle and 50 pulse waves are measured in a time span of 4 weeks and are randomly selected. The similarity between two pulse waves are analyzed with a dynamic time warping (DTW) algorithm.[40] The mapping and distance of the two pulse waves from the same volunteer and two different volunteers are shown in Figure 3b,c, respectively. For each pair of pulse waves, a DTW distance is calculated to reflect the similarity and the dissimilarity increases as the DTW distance increases. Figure 3d shows the analysis results from five volunteers, with specific distribution of the distances given in Figure S13 in the Supporting Information. The red color region indicates high dissimilarity between two pulse waves and the blue color region means high similarity. The five blue squares along the diagonal axis illustrate that the pulse waves acquired for continuous 4 weeks are stable with high similarity for the same volunteer. The other squares show the variances and dissimilarities of pulse waves from different volunteers. By setting the threshold at different distance, the identifica-tion process for the true positive rate (TPR) and false positive

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rate (FPR)[8] can be adjusted for a specific purpose, as indicated in Figure 3e and Figure S14 in the Supporting Information. A threshold with smaller distance can result in low TPR and FPR for a strict identification standard. For instance, setting the threshold to be 1 in our tests, the TPR and FPR values are 77.0% and 0.96%, respectively, which means volunteer A will be identified successfully in 770 out of 1000 trials and other volun-teers will be misidentified as volunteer A in only 10 out of 1000 trials. The differentiation and classification of the pulse wave demonstration also proves the excellent stability and precision of our pulse sensing system for possible long-term health data collections from different users.

To demonstrate possible medical assessments using our pulse sensing system, the diagnosis of arrhythmia is chosen. Arrhythmia is the phenomenon of irregular heart rhythm which can be caused by physiological and pathological fac-tors[41] and serious arrhythmia needs to be treated in time. However, arrhythmia does not happen all the time and cannot be easily diagnosed by a short-time ECG test. The users usu-ally need to wear a bulky hub (Holter monitor) with multiple

electrodes attached on the chest for at least 24 h in the hospital. Our system has excellent long-term stability with the features of lightweight, portable, and wearable for possible arrhythmia diagnosis at home.

In this work, 10 volunteers are tested as shown in Figure 4a, where the box-whisker plots are the statistic results of the peak-to-peak pulse wave intervals for each volunteer. The box is drawn as the 25th and 75th percentiles along with median value and the whiskers are drawn as the 5th and 95th percentiles. Volunteer 1 (V1) is a healthy 25-year-old male and volunteer 10 (V10) is a 28-year-old male who was previously diagnosed to have arrhythmia by the ECG setup in a local hospital. It is found that the dispersion of pulse interval of V10 is significantly larger than that of V1. The detailed pulse waves and corresponding Poincare plots for V1–V10 are shown in Figure 4b–e and Figure S15 in the Supporting Information. Poincare plots can show the dispersion of pulse intervals intuitively and quantita-tively[7] and the scattering in the Poincare plots are the two adja-cent pulse intervals. The parameters SD1 and SD2 can reveal information about the short- and long-term pulse variability and

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Figure 2. Excellent precision and stability of piezoelectret sensing system. a) Recorded pulse waves from a 28-year-old male volunteer for 180 s, inset (upper right) shows the detailed pulse waves. b) Characteristics of pulse signals for all collected data (gray) for 180 s and the averaged result (red), where ∆t is the interval time between the P1 and P2 peaks; R1 and R2 are the intensity of P1 and P2 peaks, respectively. c) The average and variation of the weekly interval time (∆t) between P1 and P2 and d) intensity ratio of P2 and P1 of a 28-year-old male volunteer for 4 weeks.



are fitted using the ellipse fitting method (dashed line), which represents the standard deviation of points perpendicular to and along the axis of the line of identity. The one who shows large values of SD1 and SD2 has the large dispersion of pulse intervals, meaning high risk of arrhythmia. For a continuous 5-d measurement, the dispersion of pulse intervals of V1 is stable and small, with SD1 between 16.6 and 18.8 ms, and SD2 between 19.7 and 25.1 ms (Figure 4f). V10, on the other hand, has unstable and large values, with SD1 between 29.6 and 41.3 ms, and SD2 between 51.8 and 83.2 ms (Figure 4g).

In another demonstration, different levels of static pressure (traditionally defined as Superficial, Middle, and Deep levels) are applied for pulse palpation, which is an important diag-nostic practice in TCM.[39] The applied specific static pressure by a pressure cuff starts from 120 mmHg and decreases to 20 mmHg with 10 mmHg intervals with each static pres-sure held for 30 s (Figure 5a). Both a pulse sensing system and a commercial optical sensor are placed on the wrist and the fingertip, respectively, for a 28-year-old male volunteer (Figure S9, Supporting Information), and the corresponding

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Figure 3. Pulse waves differentiation and classification. a) Schematic diagram showing the data processing procedures to both classify and differentiate the pulse patterns of different volunteers. The pulse signals are first truncated into a single cycle and 50 pulses are randomly selected from each of the five volunteers from collected data in a time span of 4 weeks. The similarity between two pulse waves is measured using the DTW algorithm. The mapping results between two pulses: b) from the same volunteer, and c) from two different volunteers. d) Calculated distance matrix between the long-term pulses of five volunteers. e) The differentiation results of TPR and FPR by setting the thresholds to 1, 1.5, and 2, respectively.



outputs are compared. Figure 5b shows the outputs from the pulse sensor have little drift under a large range of static pressure and the amplitude varies with the cuff pressure with two transitions during cuff deflation process. The amplitudes increase first to reach a clear maximum and decrease after-wards.[42] The conformal contact between our pulse sensor and skin is guaranteed by the static pressure applied by the inflat-able cuff.[43] On the other hand, the working principle of the

optical sensor is based on the average blood perfusion. As the static pressure increases, the amount of blood perfusion reduces and the measured amplitudes decrease. In other words, the optical pulse sensor is not suitable for the TCM application.

Above testing results also reveal that our pulse sensing system can measure the blood pressure, as indicated in Figure 5c. This result is similar to the traditional oscillometric method, in which blood pressure is measured by decreasing the cuff

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Figure 4. Arrhythmia diagnosis with the piezoelectret sensing system. a) The box-whisker plots of the pulse wave interval time for 10 volunteers. Volun-teers 9 and 10 have arrhythmia as previously diagnosed in hospitals using ECG. b) The enlarged pulse waves, and c) the corresponding Poincare plots for volunteer 1, who has no arrhythmia. d) The enlarged pulse waves, and e) the corresponding Poincare plots for volunteer 10, who has arrhythmia. Long-term Poincare plots for (f) volunteer 1, and (g) volunteer 10, respectively.


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pressure applied on the radial artery.[44,45] Specifically, the mean arterial pressure (MAP), which is the static pressure of the maximum pulse wave amplitude, is read from the pulse wave envelope directly. The diastolic pressure (DP, locating at the position with 0.85 amplitude of MAP) and systolic pressure (SP, locating at the position with 0.55 amplitude of MAP) can be calculated using the empirical fixed-ratio method[46,47] as explained in Figure S16 in the Supporting Information. The DP and SP measured by a commercial instrument (Omron BP761) are 75 and 113 mmHg, respectively. The estimated DP and SP using our pulse sensing system are 80 and 115 mmHg, respec-tively, which are close to those from the commercial pressure instrument. Since our test has a pressure interval of 10 mmHg, future experiments can record data continuously as the applied pressure is reduced to have more accurate readings of the blood pressure.

Pulse waves measured by our pulse sensing system (red) and a commercial optical sensor (blue, light source: Kingbright AM2520ZGC09, and ambient light sensor: Avago APDS-9008) under a static pressure of 30 mmHg (Figure 5d) are similar, but under a high pressure of 120 mmHg (Figure 5e), the optical

sensor fails to detect the pulse waves. The corresponding fast Fourier transformation (FFT) results also indicate that fre-quency spectrums reveal clear pulse signals for our pulse sensing system from 120 to 20 mmHg (Figures S17 and S18, Supporting Information). However, the full spectrum infor-mation can only be detected by the optical sensor under a low static pressure, as indicated in Figure 5f,g.

In the TCM diagnosis, it is believed that the health condition of human organs can be related to the pulse waves at the corre-sponding mapping points on the radial artery as defined as Cun, Guan, and Chi positions.[39] A three-channel pulse sensor array mimicking the three fingers of a TCM doctor is designed to record the pulse waves (Figure 6a). The size of each sensor is 4 × 0.8 cm2 in the prototype and its width is about the size of the fin-gertip of a TCM doctor. The detailed experimental setup is given in Figure 6b. Specifically, signals from each channel is measured and amplified, and then sampled and transmitted to a laptop for visualization and analysis. The normalized Cun, Guan, and Chi pulse wave patterns for a 26-year-old female volunteer and a 28-year-old male volunteer are shown in Figures 6c,d. Based on the TCM practice, the three sensors can differentiate all key

Figure 5. Mimicking TCM pulse palpation that needs to apply static pressure and blood pressure measurement. a) Applied static pressure from 120 to 20 mm Hg using a common pressure cuff. Inset shows the detailed experimental setup. b) Recorded pulse signals of a 28-year-old male volunteer under the applied static pressure acquired by our pulse sensing system (red) and the commercial optical sensor (blue). c) The oscillometric amplitudes of a 28-years-old male volunteer measured by our pulse sensing system, in which MAP of this volunteer is about 90 mmHg. The measured DP (locating at the position with 0.85 amplitude of MAP) and SP (locating at the position with 0.55 amplitude of MAP) are about 80 and 115 mmHg, respectively. The pulse signals (gray) and averaged pattern using our pulse sensing system (red) and the commercial optical sensor (blue) under static pressure of (d) 30 mmHg and (e) 120 mmHg. The FFT results of the recorded pulse signals of our sensing system (red) and the commercial optical sensor (blue) under a static pressure of (f) 30 mmHg and (g) 120 mmHg.



characteristics of human organs for potentially medical dia-gnosis in the future.[47] In specific, the amplitudes of the male volunteer’s pulse waves are stronger than those of the female vol-unteer at all three positions. For the same volunteer, the shapes of pulse waves from different positions are similar, while the Guan position shows the largest amplitude, followed by Cun and then Chi positions. The current work uses a commercial inflat-able cuff to apply the static pressure in a large area. Actually, it is helpful to obtain more useful physiological information if static pressure can be separately applied on each channel to better imi-tate the three-finger pulse palpation in TCM.

3. Conclusion

In summary, sandwich-structured piezoelectret with equiva-lent piezoelectric coefficient d33 up to 4100 pC/N is used to construct an active pulse wave sensing system that can be applied in m-Health systems. The excellent precision and sta-bility are validated and applied to differentiate and classify dif-ferent pulse waves from different volunteers. Several specific medical assessments have been successfully verified, including the diagnosis of arrhythmia and the detection of the blood pres-sure. Furthermore, a pulse sensing array is shown to acquire the pulse patterns from the Cun, Guan, and Chi positions, mimicking the three-finger pulse palpation in TCM.

4. Experimental SectionThe FEP/Ecoflex/FEP Sandwich-Structured Piezoelectret Film

Fabrication: The detailed fabrication steps are schematically shown in Figure S1a in the Supporting Information. The fabrication begins with

adhering the FEP film (thickness of 25 µm, American Durafilm) on a glass slide. The Ecoflex precursor (Smooth-on, Ecoflex 00–35) is coated on the FEP film by the spin-coating process (400 rpm, 1 min) and cured at 50 °C for 5 min to form an Ecoflex film (thickness of about 150 µm). Next, an array of holes (diameters of 2 mm, and center spacing of 3 mm) are punched on the Ecoflex film using a laser cutter (Universal VLS 6.60). The Ecoflex film can be easily peeled off from the FEP film due to the low surface energy of FEP film. After both sides of Ecoflex film are treated by oxygen plasma (plasma power of 210 W, O2 pressure of 350 mTorr, 20 min) to form the –OH groups, the treated film is soaked into the 10% wt. 3-aminopropyltriethoxysilane (APTES, 99% purity, Sigma–Aldrich) solution for 24 h. During the process, the –OH groups will bond with APTES to fully modify both surfaces of Ecoflex film with APTES. Two other FEP films are also treated by oxygen plasma (plasma power of 180 W, O2 pressure of 350 mTorr, 10 min). The sandwich structure is put together under a uniform pressure and temperature of 40 °C for 24 h in the final bonding process to form the chemical bond of –OH/APTES/–OH groups.[48] In this case, air is filled inside the sandwich-structured film. Moreover, as indicated in the Figure S1b,c in the Supporting Information, both Ecoflex film and FEP film are not damaged after the plasma treatment (210 W and 20 min for Ecoflex, 180 W and 10 min for FEP). In fact, the plasma treatment will increase the surface roughness such that the films turn more opaque and white.

The Pulse Wave Sensor Fabrication: The top surface of the FEP/Ecoflex/FEP sandwich-structured piezoelectret film is coated with a thin layer of Au (thickness of 50 nm) by the magnetron sputtering method. The megascopic electrical dipoles are formed afterwards by a high voltage corona charging method using a high voltage power source (Gamma High Voltage), a corona needle, and a ground electrode (Figure S2, Supporting Information). Samples are placed atop a ground electrode and situated 3 cm below the corona needle tip. A high voltage of −18 kV is applied to the corona needle to break down the air within the sandwich-structured film. The generated free charges are subsequently captured by the inner surfaces of the two FEP films for a long period of time owing to the excellent electret properties of FEP. The resulting positive and negative surplus charges on the separate FEP film layers constitute the megascopic electrical dipoles. An Al tape (thickness

Figure 6. Mimicking three-finger TCM pulse palpation. a) Mimicking the TCM pulse palpation acquisition scheme of a real doctor by using three fingers with our pulse sensing system at the Cun, Guan, and Chi positions. b) Experimental setup including signal processing, in which signals from each channel is measured and amplified, and then sampled and transmitted to a laptop for visualization and analysis. Measured typical pulse waveforms at the Cun, Guan, and Chi positions for (c) a 26-year-old female volunteer, and (d) a 28-year-old male volunteer.



of 10 µm) is adhered to the bottom surface of the film as the counter electrode and mechanical support.

Equivalent d33 Value Measurement: The equivalent quasi static d33 value is measured by a “weight moving method”.[33] Specifically, a device is placed under a metal mass (preventing triboelectric charges) with known weight, and the force (F) applied on the device is given. The transferred charges (Q) are derived by integrating the currents generated when the mass is removed immediately. Then, the quasi static d33 can be calculated by the following formula:

Quasi static /33d Q F= (1)

Characterization: The morphology of the samples is examined by a high-resolution field emission SEM (FEI Quanta 3D FEG). The output signals from the pulse wave sensor are measured using a Stanford Research Systems low-noise current pre-amplifier (Model SR570). The amplification (sensitivity) is 1 nA V−1 and the filtering setting is 12 dB low-pass filter with 3 Hz cut-off frequency (Figure S8a, Supporting Information). The output signals from SR570 are sampled by a DAQ (National Instruments PCI-6259) with sampling rate of 60 points s−1. The wireless system is based on “Arduino 101”[49] and App on the mobile device is based on “Blynk”.[50] Participants took part in experiments described herein with informed consent and no formal approval from these experiments was required.

Supporting InformationSupporting Information is available from the Wiley Online Library or from the author.

AcknowledgementsThis work is supported in part by the National High Technology Research and Development Plan of China (2015AA043505), and the Shenzhen Science and Technology Research and Development Funds (JCYJ20160411164305110 and JCYJ20150831192244849). Y.C. and H.L. gratefully acknowledge the financial support from the China Scholarship Council. Prof. X.W. and Prof. L.L. are core-principal investigators of Tsinghua-Berkeley Shenzhen Institute (TBSI) and we acknowledge the funding support of TBSI. Participants took part in experiments described herein with informed consent and no formal approval from these experiments was required. The author contribution statement was updated on October 4, 2018, following initial online publication.

Conflict of InterestThe authors declare no conflict of interest.

Keywordshealth monitoring, medical assessments, piezoelectret materials, pulse sensors, wearable systems

Received: May 17, 2018Revised: June 28, 2018

Published online: July 31, 2018

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