A Personalized Beat-to-Beat Heart Rate Detection System ...

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 13, NO. 6, DECEMBER 2019 1593

A Personalized Beat-to-Beat Heart Rate DetectionSystem From Ballistocardiogram for Smart

Home ApplicationsQingsong Xie , Student Member, IEEE, Min Wang, Yang Zhao, Student Member, IEEE, Ziran He,

Yongfu Li, Senior Member, IEEE, Guoxing Wang , Senior Member, IEEE, and Yong Lian, Fellow, IEEE

Abstract—This paper presents a ballistocardiogram (BCG)based beat-to-beat heart rate (HR) measurement system for health-care applications in smart home. The BCG signal is picked up bytwo parallel connected piezoelectric elements, and processed by arobust HR detection algorithm. To deal with the diversity in BCGsignal, a personalized heartbeat template is extracted to handlethe diversity in BCG signal. The Pearson correlation coefficientis used to correlate between personalized template and the BCGsignal. Heartbeats are detected by using the Hilbert Transform.The system is verified on 32 subjects with simultaneously recordedBCG and ECG signals. With the reference to ECG signal, we haveachieved mean absolute relative error of the beat-to-beat intervalsof 4.56%, and average absolute error±standard deviation of abso-lute error for HR estimation in a 30-seconds window of 0.59±0.56beats per minute. Meanwhile, 99.67% of the beat-to-beat intervalsare detected on average.

Index Terms—Ballistocardiogram, heart rate, hilbertTransform, piezoelectric element.

I. INTRODUCTION

BALLISTOCARDIOGRAM (BCG) records the move-ments caused by the forces associated with the contraction

of the heart and the acceleration/deceleration of blood as it isejected and moved in the large vessels [1]. It has been a popularalternative to conventional electrocardiogram (ECG) signal inmany applications, e.g., heart rate (HR) detection [2], [3], heartrate variability (HRV) analysis [4], [5], and blood pressure esti-mation [6]. Compared to the ECG measurement, which requiresat least two electrodes attached to skin [7] and it may cause skinirritation for long time use, the BCG can be measured withoutelectrode contacts, e.g., the BCG sensor can be embedded intobeds [8], [9], wheel chairs [10]–[12], or weighing scales [13]–[15]. Consequently, the key benefits of using the BCG signal

Manuscript received July 15, 2019; revised September 8, 2019; acceptedOctober 29, 2019. Date of publication December 4, 2019; date of current versionDecember 31, 2019. This work was supported by the National Natural ScienceFoundation of China under Grant 61874171. This paper was recommended byAssociate Editor Y. Zheng. (Corresponding author: Guoxing Wang.)

The authors are with the Department of Micro-Nano Electronics and theMoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shang-hai 200240, China (e-mail: [email protected]; [email protected];[email protected]; [email protected]; [email protected]; [email protected]; [email protected]).

Color versions of one or more of the figures in this article are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TBCAS.2019.2957571

Fig. 1. An example of typical BCG signal containing H, I, J, K, L, M, and Ncharacteristic points.

for HR detection come from its convenience, low cost, andease of operation. BCG-based systems offer comfortable andconvenient user experience in natural home environment.

There are several BCG-based systems for heart rate detection[16]–[19]. Fiber bragg grating (FBG) sensor is designed to ac-quire BCG for heart rate detection [16]. The performance of thissystem can be further improved since its root mean square erroris up to 6 beats per minutes (BPM). An unobtrusive system baseon BCG signal is proposed for heart rate detection [17]. The per-formance varies significantly by different subjects. To improvethe accuracy of heart rate detection, multiple-channel signalsbased systems are presented such as eight-channel BCGs [18]and BCG combined with impedance plethysmogram signal [19].These systems lose the simplicity of using the single BCG.

The detection of beat-to-beat heart rate from BCG is a verychallenging task. As shown in Fig. 1, the BCG contains sevenfiducial points (H, I, J, K, L, M, and N). Each heartbeat (HB)cycle in BCG consists of multiple peaks instead of a singlepronounced peak such as the R-peak in ECG. Therefore, thetask of detecting beat-to-beat HR in the BCG signal is muchmore complicated than detecting those from ECG.

To tackle such challenge, several methods are proposed forHR monitoring from the BCG signal [20]–[27]. The methoddescribed in [20] locates heartbeat using a BCG template bycalculating the correlation coefficient between the constructedtemplate and the signal. This method can be further improvedbecause the heartbeat template has to be manually selected.Clustering method without prior assumptions about heartbeatshape is reported to directly detect HB [21]. However, thismethod is not very practical to determine the exact HB as well asidentifying missing HB. Short-time energy (SEN) method [22]

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https://orcid.org/0000-0001-6974-2019

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1594 IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 13, NO. 6, DECEMBER 2019

Fig. 2. Overview of the proposed beat-to-beat heart rate monitoring system.

uses energy function to extract the envelope of the BCG signal,from which the HBs are identified as the local maxima. Thereare rooms for improvement in dealing with irregular waveform,which may be caused by the changes in heart rate, baselinewandering. Smoothed length transform (SLT) [23] involves thecombination of multiple windows to determine the search rangeof HB while it requires the prior knowledge to experimentallyadjust many thresholds.

Two methods based on HB templates are reported re-cently [24], [25]. The first method uses clustering [24] to extractHB template and determines the HB based on the best fit, wherethe performance depends on multiple empirical parameters. Thesecond method uses extended function of multiple instancesalgorithm to learn individual HB template, from which theHBs are confirmed through comparing confidence with presetthresholds [25]. It requires human intervention for learning theHB pattern. Frequency domain analysis techniques, such asHilbert Transform (HT) [26], the combination of HT and phasevocoder (HTPV) [27], are utilized in BCG signal processingaiming for better performance. However, these methods are notable to provide beat-to-beat HR measurements, which are crucialfor HRV analysis.

In this paper, we present an automatic beat-to-beat heartrate detection system using BCG signal. The BCG signals areobtained from a piezoelectric based sensor. A clustering methodis utilized to automatically extract the personalized heartbeattemplate from the given BCG signal. The correlation coefficientis adopted to describe linear correlation between the extractedtemplate and the BCG signal. Heartbeats are detected by HilbertTransform function.

The rest of this paper is organized as follows. Section II de-scribes the proposed system architecture. Section III presents theproposed sensor. Section IV details the proposed novel detectionalgorithm. Section V gives experimental results. Conclusion isfurther made in Section VI.

II. PROPOSED SYSTEM ARCHITECTURE

An overview of the proposed system architecture for beat-to-beat heart rate detection is shown in Fig. 2. It primarily containstwo parts: BCG sensor for BCG acquisition and algorithm forbeat-to-beat HR detection. For the BCG sensor, a piezoelectricbased BCG sensor is designed to convert vibrations of the bodyinto electrical signal. The signal conditioning circuit converts

Fig. 3. The piezoelectric sensor used for on-chair BCG detection.

charge from piezoelectric element to a voltage signal, amplifiesit, and removes the noises. The voltage signal is digitized viaa 12-bit analog-to-digital converter (ADC). The digital signalis sent to computer (PC) via UART, which is controlled byMicrocontroller Unit (MCU).

A robust algorithm is designed to detect beat-to-beat heartrate, which primarily consists of preprocessing, heartbeat tem-plate extraction, heartbeat detection, and template adjustment, asshown in the right of Fig. 2. Out-of-band frequency is eliminatedin preprocessing stage. In heartbeat template extraction stage,BCG heartbeat template is extracted using a short duration ofBCG signal. Subsequent heartbeats are then detected in heartbeatdetection stage. In template adjustment stage, heartbeat templateis updated to adjust to the latest heartbeat pattern based on newlydetected heartbeats.

III. PROPOSED BCG SENSOR

The proposed BCG sensor hardware is shown in Fig. 3, whichcontains a sensor and a signal conditioning circuit board. In thefollowing sections, we will present the hardware details.

A. Piezoelectric Element

Piezoelectric elements generate electrical charge signal re-sponding to the vertical acceleration of body mass due to positive


XIE et al.: PERSONALIZED BEAT-TO-BEAT HEART RATE DETECTION SYSTEM FROM BCG FOR SMART HOME APPLICATIONS 1595

Fig. 4. The schematic of the interface circuit.

piezoelectric effect [28]. The amount of charge output (Q) bythe piezoelectric element is proportional to its force F , definedas:

Q = dF = dma, (1)

where d is piezoelectric constant, about 500 pC/N, m and a aretotal mass and acceleration of the object, respectively.

However, this piezoelectric element has small sensing areawhereas the contact area between the human body and the seatsurface is large. Therefore, it is not suitable for direct monitoringof BCG. To address such problem, the piezoelectric element issandwiched between two steel plates in order to increase thesensitivity. The steel plates are fixed by eight screws. To ensuresteel plates smoothly move up and down with the vibration ofthe human body such that vibration signal can be transmitted topiezoelectric element, each screw is covered with a spring. Thisdesign has two advantages: One is that the pressure generated bythe vibration of the human body is uniformly dispersed by thesteel plate, thereby preventing excessive bending of the ceramicpiece inside the sensor due to uneven distribution of the pressure.The other advantage is the pressure is evenly distributed on theforce receiving surface of the sensor. It enlarges sensing arearesulting in better sensitivity.

To detect left-to-right (or right-to-left) body movement, twopiezoelectric elements are symmetrically placed at each sideof the sensor. Moreover, four auxiliary rubber mats are placedat four corners of the steel plates to support the piezoelectricelement. To further enhance the performance, each pizeoelectricelement is assisted by two rubber mats to reduce the impact fromfront-to-back swing.

B. Conditioning Circuit

The electrical charge generated by the piezoelectric elementrequires an interface circuit having high-input impedance andlow input bias. For this purpose, a 3-stage signal conditioningcircuit is designed to process the output of the two parallelconnected piezoelectric elements, as shown in Fig. 4. Thefirst-stage charge amplifier converts electrical charge generatedby the sensor to a voltage signal. Meanwhile, capacitor Ca isused to balance the charge injected into negative input throughcharging itself. It means that only dynamic charge signal willdevelop voltage signal through the amplifier. Therefore, theconstant force like gravity has almost no impact on the voltageoutput. Besides, the large motion artifacts or sudden impactforce could result in the saturation of amplifier, the gain of the

first-stage is set to 1 by choosing resistor Rf equal to Ri. Thesecond-stage circuit is a RC bandpass filter. The low and highcutoff frequencies (fL, fH ) are determined by:

fL =1

2πR1C1(2)

fH =

√√2− 1

2πR2C2, (3)

Since the useful BCG signal is contained within 0.1-10 Hz, wecan get R and C values as follows: R1=74 KΩ, C1=22 μF,R2=100 KΩ, and C2=100 nF. With the bandpass filter, thenoises, such as power line interference and muscle contraction,are reduced. Since motion artifacts with the same frequency havehigher amplitude than BCG, the third stage circuit is designed

with a gain ofCi

Cf= 10 to avoid saturation.

IV. BEAT-TO-BEAT HEART RATE DETECTION ALGORITHM

A. Preprocessing

In real applications, BCG signal is contaminated by variouskinds of noises, including respiration, power line interferenceand muscle contraction. A Butterworth bandpass filter is thusemployed with cutoff frequencies of 1 and 10 Hz to eliminatehigh-frequency noises and typical respiration signal which isbelow 0.5 Hz.

B. Heartbeat Template Extraction

The amplitude of fiducial points changes from beat to beat.Thus, it is difficult to identify a fixed peak even the most promi-nent J-peak from the BCG. One possible solution to address thisproblem is to extract a personalized BCG heartbeat templateand then detect the peak position as the heartbeat that bestmatches the template during each cycle. Beat-to-beat heart rateis calculated from two consecutive heartbeats.

In order to extract a personalized BCG heartbeat template, thefirst 30-seconds (sec) training BCG is analyzed to extract thetemplate of the signal. An adaptive threshold (ADT) method,which is used for detecting the peaks of photoplethsmographysignal in [29], is adopted for finding the potential J-peaks inthe training BCG signal. In this manner, the virtual threshold iscontrolled by original BCG amplitude, which is decreased withthe fixed slope parameter when it is larger than BCG amplitude.When it meets the signal, the threshold is accompanied withBCG amplitude until it reaches to maximum point. ADT helpsto identify potential J-peaks, but it may detect false targetsdue to multiple peaks in each cycle. Moreover, it is hard todetect the true heartbeats if the BCG amplitude changes abruptlyowing to baseline wandering, which is caused by respiration,thermoregulation, and sympathetic nervous system activities.

Therefore, we need to select true heartbeats to form heartbeatpattern. According to the detected heartbeat positions, BCGsignal is segmented into windows of the length of typical pulseinterval of human body (1 second). Each potential J-peak is cen-tralized to the middle of the window. It is reasonable to assumethat the abnormal BCG segments center on false J-peaks display



more morphological randomness. Thus, their patterns differfrom normal BCG segments. As a result, if a group of segmentsdescribe a similar signal pattern, heartbeats are more likely to beclustered together owning to a better inter-beat consistency whilethe abnormal BCG segments tend to be partitioned into multipleclusters due to much more diverse morphologies. Hence, atypical clustering algorithm K-medoids [30] is used to learn apersonalized heartbeat template from raw BCG with k-means++algorithm [31] choosing initial cluster centroids. In order toreduce computation load, the Euclidean distance is selected toquantify the dissimilarity between two segments. The number ofclusters is selected as 6 for generating the template from trainingdata. This is because the user is asked to keep still at the first30 sec to minimize motion artifacts, and hence training datacontain at least 6 heartbeats.

The cluster that contains most objects is selected as the can-didate cluster for the heartbeat template. The final personalizedheartbeat template is then obtained by point-wise average of allthe BCG segments in candidate cluster. Fig. 5 illustrates theprocess of the heartbeat template learning process. Fig. 5(a)shows a 30-sec training BCG signal. J-peaks are roughly de-tected using ADT method, as shown in Fig. 5(b). All the BCGsegments are presented in Fig. 5(c). The densest cluster is shownin Fig. 5(d), in which abnormal BCG segments are eliminated.Fig. 5(e) gives the heartbeat template which is obtained by takingpoint-wise mean of the segments in the densest cluster. It can beseen from Fig. 5 that good quality of the heartbeat template isacquired.

C. Heartbeat Detection

With the introduction of personalized heartbeat template, weare able to identify the heartbeat by finding the best match inevery heartbeat cycle. This is done by computing the Pearsoncorrelation coefficient (PCC) (4), which describes the similaritybetween the heartbeat template and incoming BCG signal,

PCC =

∑Nk=1(X(k)−X)(Y (k)− Y )

√∑Nk=1(X(k)−X)2

∑Nk=1(Y (k)− Y )2

, (4)

where X , Y are two random variables, denoting BCG templateand BCG segment. X , Y are average values of X and Y ,respectively. N is the total sample number of the template.

If the incoming BCG segment has high similarity with thetemplate, it will reflect as a high value of PCC. Eminent peaksin this PCC envelope appear at the locations where the patternis described by the template. Therefore, these peaks can be con-sidered as heartbeats. Fig. 6 gives a representative plot of PCCsignal. Raw BCG signal is shown in Fig. 6(a) while heartbeattemplate is presented in Fig. 6(b). PCC signal measuring thesimilarity between the heartbeat template and BCG signal isgiven in Fig. 6(c), which shows that the large peaks in PCC signalappear at the positions that match template best during everycycle of BCG. As a result, detecting these peaks as heartbeatsbecomes easier.

After obtaining PCC signal, PCC is passed through a non-linear transformation to obtain positive peaks. The main target

Fig. 5. An example of heartbeat template learning. (a) 30-sec BCG segment.(b) Potential J-peaks identified by ADT. (c) All 1-sec segments overlaid.(d) BCG segments of the densest cluster. (e) Heartbeat template extracted bytaking the point-wise average of the segments in the densest cluster.

is to enhance the heartbeat amplitude. Inspired by [26], [27],BCG data is modelled as pulse envelope modulated by sinusoidalwaveform. Therefore, PCC signal has similar component sinceit estimates similarity between the template and incoming BCGdata, which is expressed by (5).

PCC(m) = a(m)cos(2πf0 m), (5)

where a(m) represents the pulse envelope of PCC signal and f0is modulation frequency.

To extract envelope a(m) of PCC signal from its modulatedsignal, we propose to use Hilbert Transform (HT). When thefrequency of a(m) is narrow compared to modulation frequencyf0, the output of HT (HTPCC) is (6).

HTPCC(m) = HT(PCC) = a(m)sin(2πf0 m) (6)



Fig. 6. An example of PCC signal. (a) BCG signal. (b) Heartbeat template.(c) PCC signal.

Therefore, magnitude square (a(m)2) of the envelope is attainedwith:

a(m)2 = PCC(m)2 + HTPCC(m)2. (7)

Then, the filtering is applied to obtain sharp peaks aroundheartbeat regions and smooth out the spikes and noise bursts.It is implemented by a rectangular impulse response, h(m). Thefiltering operation is performed in both the forward and reversedirections to avoid phase distortion. Generally, the rectangularimpulse should have the same duration as IJK complex orpossibly wider duration. This study uses 0.32-second durationof h(m) which covers IJK complex. Fig. 7 illustrates the per-formance of HT to extract envelope of PCC signal. It clearlyshows that major local maxima in smooth envelope indicateapproximate locations of the heartbeats.

Heartbeat locations in PCC signal may not coincide withsmooth envelope peaks strictly. To eliminate this difference, atrue peak detector is incorporated to find the correct time instantsof the heartbeats. The heartbeats are identified by searchingthe largest amplitude in PCC within ±200 ms of the identifiedlocations of the smooth envelope peaks in the previous step. Anexemplary plot of the proposed algorithm is shown in Fig. 8.Fig. 8(b) demonstrates PCC signal has a prominent peak duringevery cycle, which is more explicit than J-peak. Fig. 8(d) illus-trates that the peaks in smooth envelope lie in the neighbourhoodof PCC peaks. Fig. 8(e) describes that heartbeats are detectedby searching the maxima in PCC signal in the neighbourhood of

Fig. 7. The performance of Hilbert Transform. (a) PCC signal. (b) Theenvelope of PCC signal which is extracted by Hilbert Transform. (c) Smoothenvelope.

smooth envelope peaks. R-peaks in ECG offer the reference forheartbeat detection. It can be clearly seen that the beat-to-beatintervals (BBI) in BCG are almost identical to R-R intervals(RRI) in ECG even without clear J-peaks. The ADT method,shown in Fig. 8(f), misses many heartbeats.

D. Template Adjustment and Refinement

Due to changes in average heart rate, baseline drift, and thecomplex dynamics of heart activity, the shape of the heartbeattemplate varies over time. To address this problem and update thechanges in template, the template is re-estimated every 100 newheartbeats detected in previous stages. For every heartbeat, a newsegment is extracted with the same length as previous templatecentered at the heartbeat. An updated template is obtained bytaking clustering of all the 100 segment data as used before.

The beat-to-beat intervals detected by the above method areprobably incorrect due to complex variations of heart activity orpoor quality. These erroneous intervals are removed if they aresmaller than 0.5 times of median value of the previous 5 detectedbeat-to-beat intervals as heart rate is a slowly time varying signal.This limit has been chosen as a suitable tradeoff to detect as manybeat-to-beat intervals as possible and reduce as many erroneousbeat-to-beat intervals as possible.



Fig. 8. Proposed heartbeat detector. (a) Raw BCG signal. (b) PCC signal.(c) HT envelope. (d) Smooth envelope. (e) Detected heartbeat by our method.(f) Detected heartbeat by ADT.

Fig. 9. Experiment setup, where BCG and ECG were simultaneously recorded.

V. EXPERIMENT RESULTS

A. Dataset

In order to evaluate the performance of the proposed system,we recruited 32 subjects for data recording with age in 24±2.6years old and weight in 59±10.6 kilogram (Kg). An ECG record-ing using electrodes attached on the chest was simultaneouslyrecorded for reference purpose, as shown in Fig. 9. During dataacquisition phase, the subjects sit on the proposed sensor, whenthey keep still as much as possible. In order to collect abruptchanges of signal due to complex dynamic variations of heartactivity and obtain high values of heart rate samples. 20 subjectswere required to run 10 minutes at speed of 8 kilometers/houron treadmill prior to data recording. After that, the subjects wererequested instantly to sit on the sensor for data recording. Datawere sampled at frequency of 250 Hz and sent to PC via UART.The experiment lasted at least 12 minutes (min) for every subject.

B. Metrics

For measuring the accuracy of the proposed algorithm, theR-peak in synchronized ECG signal serves as a reference, whichis detected by [32] and manually corrected. The performance ofthe method is evaluated with four quantitative metrics:

MAE (mean absolute error) and SDAE (standard deviationof absolute error): MAE and SDAE are used to evaluate theaccuracy for heart rate estimation, which are defined as follows:

MAE =1

N

N∑

i=1

|HRBCG(i)− HRECG(i)|, (8)

SDAE =

√1

N

∑N

i=1(|HRBCG(i)− HRECG(i)| − MAE)2,

(9)

where HRBCG is estimated HR from BCG, HRECG is groundtruth HR from ECG, and N is total number of samples.

Coverage: The ratio between the number of detected beat-to-beat intervals from BCG and the number of detected R-Rintervals from ECG. Coverage indicates the ratio of beat-to-beatintervals of BCG that are detected.

MARE (Mean absolute relative error): MARE is to as-sess the performance of beat-to-beat heart rate estimation,



TABLE IMAE±SDAE (BPM) FOR HEART RATE ESTIMATION USING DIFFERENT METHODS

defined in (10),

MARE =1

N

N∑

i=1

|BBI(i)− RRI(i)|RRI(i)

∗ 100%, (10)

where N is the number of BBI.

C. Results

MAE and SDAE for heart rate estimation using the proposedmethod and other recent methods in [22], [23], [26], [27] aresummarized in Table I. Heart rate is computed over 30 secondsusing a sliding window with 15 seconds of overlap as [27]. Theproposed method achieves the best results, where MAE andSDAE are 0.59 BPM, 0.56 BPM, respectively. SEN [22] detectsthe maxima of the envelope extracted by energy function asheartbeats. SLT [23] applies multiple windows to determine asearch range to detect heartbeats from BCG. Both methods facedifficulties in identifying heartbeats if the BCG signal qualityis poor, which is usually reflected by missing or over-detectedheartbeats. HT [26] and HTPV [27] utilize Hilbert Transformto extract BCG envelope to obtain the significant frequencycorresponding to HR. However, the significant frequency woulddeviate from true HR for poor quality signals. With the intro-duction of personalized template, we are able to better identifyheartbeats even though the BCG signal quality is low, e.g., forrecordings 24 and 32.

Fig. 10 shows the correlation (a), and Bland-Altman plot(b) between estimated HR from BCG and true HR from ECG.Fig. 10(a) shows our system achieves Pearson correlation coef-ficient of 0.996. Fig. 10(b) indicates the majority of erroneous

Fig. 10. (a) Heart rate estimates from BCG versus true heart rate from ECG.(b) Bland-Altman plot of the difference between estimated HR and true HR.



TABLE IITHE PERFORMANCE OF BEAT-TO-BEAT HEART RATE ESTIMATION

Fig. 11. The signals with different quality. The heartbeat shapes are muchclear in the top signal while the signal in the bottom shows irregular pattern.

estimates lie in the agreement area, which is denoted by mean er-ror ± 1.96 times of standard deviation of error (ME±1.96STD).

A distinct advantage of the proposed method is its ability tooffer beat-to-beat HR. Table II shows the coverage, and MAREfor each subject, respectively. The results show that the proposedalgorithm is able to achieve beat-to-beat heart rate detection fromthe BCG signals of all subjects. On average, Coverage of 99.67%and MARE of 4.56% are achieved.

The testing subjects have different health condition, heartactivity, and respiration rate, which lead to significant variationsof signal quality. Therefore, the performance of our systemvaries among different subjects, especially MARE and Cov-erage. Generally, the subjects with discernible heartbeat shaperesult in better performance. In the best case of our study, 100%beat-to-beat intervals are detected, and 1.32% of MARE and0.14±0.44 BPM of MAE±SDAE are achieved. While in theworst case, only 97.59% beat-to-beat intervals are detected andMARE is 7.86%. The main reason is illustrated in Fig. 11, wheretypical signals from high MARE and low MARE are shown.

TABLE IIITHE PERFORMANCE OF BEAT-TO-BEAT HEART RATE ESTIMATION

It can be obviously observed that the signals with clear heartbeatpattern lead to better performance.

We have studied the effect of sex on the performance of theproposed system. The average MAE, SDAE, Coverage, andMARE are listed in Table III. It can be seen there is insignificantimpact of sex on our system.

VI. CONCLUSION

In this paper, a beat-to-beat heart rate monitoring systemwhich contains a BCG sensor with a signal conditioning circuit,and a beat-to-beat heartbeat detection algorithm has been pro-posed. The proposed system can be embedded to chair or bed forHR monitoring. Furthermore, we proposed a robust method forbeat-to-beat heart rate detection, which consists of clusteringto automatically learn personalized heartbeat template fromBCG signal for each subject, Pearson correlation coefficient andHilbert Transform for subsequent heartbeat identification. Theexperiment conducted on 32 subjects shows that the proposedsystem achieves mean MAE±SDAE of 0.56±0.59 BPM whiledetecting 99.67% of the beats from the BCG on average.

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Qingsong Xie received the B.S. degree in electronicinformation engineering from the University of Elec-tronic Science and Technology of China, Chengdu,China, in 2015. He is currently working toward thePh.D. degree in microelectronics at Shanghai JiaoTong University, Shanghai, China.

His research interests include machine learning,computer vision, and signal processing.

Min Wang received the M.S. degree in integratedcircuit engineering, in 2018 from Shanghai Jiao TongUniversity, Shanghai, China, where he is currentlyworking toward the Eng.D. degree in electrical andinformation engineering. His research interests in-clude wearable health monitoring system design andbiological signal processing.

Yang Zhao received the B.S. and M.S. degrees inelectrical engineering from Xian Jiaotong University,Xian, China, in 2015, and Ph.D. degree in electri-cal engineering from York University, Toronto, ON,Canada, in 2019. He is currently a Postdoctoral Re-searcher with York University. His research interestsinclude ultralow power circuits and system for nextgeneration wearable biomedical sensors.

Ziran He received the M.E. degree in integrated cir-cuit engineering from Shanghai Jiao Tong University,Shanghai, China, in 2019. He is currently working asa System Design Hardware Engineer with TP-LINKtechnology company.


https://dx.doi.org/10.1109/JBHI.2019.2901635


Yongfu Li (S’09–M’14–SM’18) received the B.Eng.and Ph.D. degrees from the National University ofSingapore (NUS), Singapore.

He is currently an Associate Professor with theDepartment of Micro and Nano Electronics Engi-neering and MoE Key Lab of Artificial Intelligence,Shanghai Jiao Tong University, Shanghai, China. Hewas a Research Engineer with NUS, from 2013 to2014. He was a Senior Engineer from 2014 to 2016,Principal Engineer from 2016 to 2018, and Memberof Technical Staff from 2018 to 2019 with GLOB-

ALFOUNDRIES, as a Design-to-Manufacturing (DFM) Computer-Aided De-sign Research and Development Engineer. His research interests include ana-log/mixed signal circuits, data converters, power converters, biomedical signalprocessing with deep learning technique, and DFM circuit automation.

Dr. Li was the recipient of the Singapore Economic Development BoardGLOBALFOUNDRIES Graduate Scholarship. He is actively contributing tothe Singapore Section Young Professionals Affinity Group and has served as theSecretary from 2012 to 2013 and the Chair from 2014 to 2015.

Guoxing Wang (M’06–SM’13) received the Ph.D.degree in electrical engineering from the Universityof California, Santa Cruz, Santa Cruz, CA, USA, in2006.

He was a Member of Technical Staff with AgereSystems, San Jose, CA, USA, from 2006 to 2007.From 2007 to 2009, he joined the Second Sight Med-ical Products, Sylmar CA, USA, where he designedthe integrated circuits chip that helped blind peopleto restore vision. He is currently a Professor with theSchool of Microelectronics, Shanghai Jiao Tong Uni-

versity, Shanghai, China. He has authored/coauthored in various peer-reviewedjournals and conferences. His current research interests include biomedicalelectronics and bio-inspired circuits and systems.

Dr. Wang has been the Deputy Editor-in-Chief for the IEEE TRANSACTIONS

ON BIOMEDICAL CIRCUITS AND SYSTEMS since 2016 and a Vice President ofthe IEEE Circuits and Systems Society since 2019. He was an Associate Editorfor the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II from 2012 to 2015,Guest Editor for the IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN

CIRCUITS AND SYSTEMS and Guest Editor for the IEEE TRANSACTIONS ON

BIOMEDICAL CIRCUITS AND SYSTEMS, both in 2014. He was the TechnicalProgram Chair for the IEEE Conference on Biomedical Circuits and Systemsin 2016. He was the Local Chair for the first IEEE Green Circuits and Systemsand for the second Asia Pacific Conference on Postgraduate Research in Micro-electronics & Electronics, both in 2010.

Yong Lian (M’90–SM’99–F’09) has research in-terests that include biomedical circuits and systemsand signal processing. He was the recipient of morethan 15 awards for his research, including IEEE Cir-cuits and Systems Society’s Guillemin–Cauer Award,IEEE Communications Society Multimedia Commu-nications Best Paper Award, Institution of EngineersSingapore Prestigious Engineering AchievementAward, and Winning of the Design Contest Awardin ISLPED 2015. He is the President of the IEEECircuits and Systems (CAS) Society, Chair of the

IEEE Periodicals Partnership Opportunities Committee, member of the IEEEPeriodicals Committee, member of the IEEE Biomedical Engineering AwardCommittee, and member of Steering Committee of the IEEE TRANSACTIONS ON

BIOMEDICAL CIRCUITS AND SYSTEMS. He was the Editor-in-Chief for the IEEETRANSACTIONS ON CIRCUITS AND SYSTEMS Part II for two terms. He served asthe VP for Publications and VP for Region 10 of the IEEE CAS Society, andmany other roles in IEEE. He is the Founder of the IEEE Biomedical Circuits andSystems Conference and the Asia Pacific Conference on Postgraduate Researchin Microelectronics and Electronics (PrimeAsia). He is a Fellow of the Academyof Engineering Singapore.


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