VVRRM: Vehicular Vibration-based Heart RR-IntervalMonitoring System

Amelie BondeCarnegie Mellon University

Electrical and Computer EngineeringMoffett Field, California

amelie@cmu.edu

Shijia PanCarnegie Mellon University

Electrical and Computer EngineeringMoffett Field, Californiashijiapan@cmu.edu

Zhenhua JiaRutgers University

Wireless Information NetworkLaboratory

North Brunswick, New Jerseyzhenhua@winlab.rutgers.edu

Yanyong ZhangRutgers University

Wireless Information NetworkLaboratory

North Brunswick, New Jerseyyyzhang@winlab.rutgers.edu

Hae Young NohCarnegie Mellon University

Civil and Environmental EngineeringPittsburgh, Pennsylvania

noh@cmu.edu

Pei ZhangCarnegie Mellon University

Electrical and Computer EngineeringMoffett Field, Californiapeizhang@cmu.edu

ABSTRACTContinuous heart rate variability (HRV) monitoring in cars canallow ambient health monitoring and help track driver stress andfatigue. Current approaches that involve wearable or externallymounted sensors are accurate but inconvenient for the user. Inparticular, prior approaches often fail when noise from humanmotion or car noise are present.

In this paper, we present VVRRM , an ambient heartbeat moni-toring system in an automobile which uses a set of accelerometersin a car seat to monitor a subject’s heart RR-intervals. The sys-tem removes high energy motion noise and periodic noise using acombination of peak detection with extracted wavelet coefficients.Furthermore, it tracks human heart locations through sensor se-lection to maximize the heart signal energy. We tested the systemwith both a manufactured heartbeat signal and experiments withhuman subjects. Overall our mean absolute error for RR-intervalestimation was 54 ms across all human subjects, and 3 ms with ourmanufactured heartbeat signal.

CCS CONCEPTS•Human-centered computing→Ubiquitous andmobile com-puting systems and tools;

KEYWORDSvibration; heart rate variability (HRV); vehicular sensing; humancondition monitoring

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACMmust be honored. Abstracting with credit is permitted. To copy otherwise, or republish,to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.HotMobile ’18, February 12–13, 2018, Tempe , AZ, USA© 2018 Association for Computing Machinery.ACM ISBN 978-1-4503-5630-5/18/02. . . $15.00https://doi.org/10.1145/3177102.3177110

ACM Reference Format:Amelie Bonde, Shijia Pan, Zhenhua Jia, Yanyong Zhang, Hae Young Noh,and Pei Zhang. 2018. VVRRM: Vehicular Vibration-based Heart RR-IntervalMonitoring System. In Proceedings of 19th International Workshop on MobileComputing Systems & Applications (HotMobile ’18). ACM, New York, NY,USA, 6 pages. https://doi.org/10.1145/3177102.3177110

1 INTRODUCTIONContinuous heart rate and heart rate variability (HRV) monitoringin cars can allow for ambient health monitoring in an environmentpeople inhabit regularly. Short-term heart rate variability moni-toring in terms of RR-intervals (the distance between successiveheartbeats) is particularly useful. It has been used to help track stressand fatigue, which are important driver safety issues [4, 13, 16].

Prior work takes a number of approaches including photoplethys-mography, electrocardiography (ECG) and cameras, sometimes incombination, to retrieve heart rate from people [8, 9, 14, 18]. Thesemethods can be useful for heart-rate monitoring in static envi-ronments but are inconvenient to the user or are inaccurate forRR-interval measurements. In particular, these approaches fail forhigh-noise in-car environments subject to periodic noise such ascar engine vibration and impulsive noise from subject movement.

We present VVRRM , a non-intrusive, heart RR-interval mea-surement system for the in-car environment. VVRRM first collectsaccelerometer data from our sensing module embedded in car seats.We then remove high-energy non-periodic motion segments result-ing from body motion and periodic noise resulting from the engineand other noise. We choose between multiple sensors to accountfor variations in body size and position. Finally, we perform peakdetection to obtain the RR-interval using the distance between thepeaks.

We chose to study an idling car instead of a driving scenariobecause we wanted to focus on engine and human motion noisebefore tackling the noise caused by the car’s movement on the road.We felt the noise of the moving car would overwhelm the othertypes of noise and should be studied separately.

Our paper provides three contributions:

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Figure 1: System Overview

(1) We introduceVVRRM , a real-time system that uses ambientcar seat vibration to monitor the RR-intervals of a subject ina (currently stationary) car.

(2) We characterize the motion noise caused by human move-ment and the motion noise caused by an idling car andpresent an adaptive sensing algorithm to detect the heartbeatsignal in the midst of high levels of noise.

(3) We present a comprehensive evaluation of our system usingboth characterization data with heartbeat recordings andhuman subject experiments.

The rest of this paper is organized as follows: We discuss relatedwork in Section 2, and also discuss background information relatedto heart rate and heart rate variability. Section 3 describes ouroverall system, and we describe our evaluation and go over resultsin Section 4. Finally, in Section 5 we conclude our work.

2 BACKGROUND AND RELATEDWORKDefinition of Heart RR-Interval: At every beat, the heart is po-larized and depolarized to trigger its contraction, an electrical ac-tivity which can be measured by an ECG. The depolarization ofthe main mass of the ventricle causes the largest peak in an ECG,called the R wave. The RR-interval is defined as the distance be-tween the peaks of two R waves and describes the duration of onecomplete cardiac cycle. The peaks of the waves VVRRM measurescorrespond to the heart’s movement, and the distance between thetwo peaks is the same as the RR-interval.

Heart RR-interval as an Indicator of Stress: As stress levelincreases, the heart rhythm is affected and becomes unstable [3].This instability is reflected in RR-interval measurements [11]. ThusRR-intervals provide a key indicator of stress. Prior work in themedical domain has defined that the error of this indicator needsto be lower than 100ms [6].

Monitoring Methods: Various ways of monitoring heart ratehave been explored in the noisy vehicle environment. One com-mon approach relies on the use of electrocardiogram (ECG) sen-sors [5, 9, 17]. These approaches, in general, require mounting thatallows direct skin contact between the driver and the sensors toavoid unacceptable noise levels. While their performance is promis-ing, this mounting requirement prevents these approaches frombecoming mainstream.

Wearable devices can be inconvenient for the user, so we pre-fer an approach that is integrated with the car. A variety of ap-proaches have been tried including the use of capacitive sensors inthe seat, high-resolution cameras to capture skin tone fluctuations,

and thermal imaging. While these approaches work well in a staticenvironment in the lab, in the vehicle they are subject to non-idealsituations (e.g. light conditions, vibrations, etc.), these approachesfail to provide accurate measurements of individual heartbeat inter-vals [2, 14, 18], which is not useful for stress and fatigue inference.Placing sensors in a steering wheel is also subject to real-worldproblems, as drivers place their hands in different positions andoften wear gloves in cold weather[8].

In non-vehicle environments, vibration-based approaches havebeen utilized in beds [7]. These approaches utilize the mechanicalmotion of the heart to detect and sense heartbeat without intru-sive sensors. These approaches hold promise for non-skin-contactsensing. Unlike vibration-based approaches, capacitive sensors thatare not worn next to the body can be sensitive to environmentalnoise, including noise that does not involve motion (e.g., humidity,the distance of body parts from the sensor), while accelerometersonly measure motion from the parts of the body they are in contactwith. (e.g., if a person moves a part of the body not in contact withthe accelerometer, such as their arms, the sensor values do notchange significantly due to motion). This means that when usingaccelerometers, the noise sources would be greatly reduced. Usingaccelerometers has some limitations: along with motion noise andplacement concerns, which we address, they may be sensitive toclothing thickness.

3 SYSTEM DESCRIPTIONVVRRM accurately detects the vibration of heartbeats throughthe body’s forces on the car seat in an automobile to measureRR-intervals. This poses many challenges which our system ad-dresses with a multifold approach. Our main challenges are thesometimes low signal to noise ratio (heartbeat forces on the seatsare small), human motion noise (movement noise can overwhelmthe signal), engine noise (periodic engine noise increases the noisefloor), and sensor placement (the best location to capture the heartmotion varies between persons and over time. We can intuitivelyunderstand this because people are different heights and they sit indifferent ways). Figure 1 shows the system overview. The sensingmodule acquires the signal using a grid of accelerometers to capturethe heart motion that can occur at different locations. The targetsignal extraction module removes high motion parts induced byhuman motion. Then it performs denoising on the remaining sig-nal to remove periodic noise (e.g. engine noise). Next, it smoothsthe signal to enhance peaks, followed by a wavelet filter to fur-ther enhance the impulse signal in the heart rate frequency range.

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Figure 2: Raw data for a human subject with (a): engine off,(b): engine on, and (c) with subject laughing. Red boxesmarkthe heartbeat location. With no engine noise, the heartbeatsignal in (a) is fairly clear. In (b), it is harder to see becauseof engine noise, while in (c), it is obscured by noise fromlaughter.

Finally, the RR-interval estimation module addresses the sensorplacement challenge with a sensor selection algorithm, and outputsthe final RR-interval estimation.

3.1 SensingVVRRM uses a set of inertial sensors to pick up movement causedby the subject’s beating heart as the subject sits in the seat. Theheartbeat causes their chest and stomach to vibrate. Because wedetect the vibration of heartbeats from the vibration of the seat,the location of the sensor relative to the body greatly impacts thesignal magnitude. Intuitively, the closer the sensor is to the heart,the stronger the signal. This raises two challenges: 1) People aredifferent heights, so when they sit in the car their hearts are in dif-ferent locations. And 2) people sometimes shift position and do notalways sit leaning back. To address the first challenge, we use a net-work of sensors that lie against the backrest of the car seat and canpick up signals from a larger area. To address the second challenge,we use a sensor in the seat belt to pick up the stomach vibrationcaused by the heartbeat. We choose a sparse sensor array insteadof many sensors for a lower-cost and low-computational-powerdesign. We use piezoelectric accelerometers, which are excellent forvibration monitoring due to their wide frequency response, linearfrequency response curve, and high sensitivity. We describe thesensor selection algorithm in Section 3.3.1.

Accelerometers are very sensitive to vibration and changes inmotion: this allows us to see the very small signal caused by themotion of the beating heart. Figure 2(a) shows raw data from ahuman subject sitting in a car. We can see clear heartbeat-inducedvibrations as small peaks marked by the dashed red boxes. Thatsame sensitivity also makes accelerometers very sensitive to vibra-tion and motion noise. Figure 2(b) shows a human subject sittingin an idling car. The engine noise makes the heartbeat signal lessclear. Figure 2(c) shows a human subject laughing. The humanmotion noise of laughter completely overwhelms the heartbeatsignal. We describe how VVRRM handles these types of noise inthe following sections.

Figure 3: As we lower our noise threshold and keep less data,our error lowers and then increases.

3.2 Target Signal ExtractionThe target signal extraction module handles both human motionand engine noise. It first extracts windows of data where there isless human motion noise. It then applies denoising and a waveletfilter to remove engine noise.

3.2.1 Windowing for Human Motion Noise Extraction. Our firststep is recognizing and discarding portions of the signal that areexcessively noisy due to person movement. We found that mostlarge spikes of noise in the data were due to person movement,either talking, coughing, laughing, gesturing, or shifting positions.To identify this type of signal, we use a sliding window on the vi-bration signal and extract the maximum value of the window. If thisvalue is above a threshold, we skip one second of data (i.e., label it asmotion noise) and try again with a newwindow, moving forward byone second each time until we have a window that doesn’t exceedour threshold. We skip one second at a time because we observeexperimentally that noise in the data tends to last from between halfa second to several seconds and takes about half a second to subside.We set our threshold by fitting a probability distribution to the firstminute of data for each person using kernel density estimation andthen using the inverse cumulative distribution function (ICDF) tocompute a threshold to detect high motion noise. The thresholdis determined empirically, considering the tradeoff between datapreservation and accuracy (i.e., high threshold leads to more databut lower accuracy, while low threshold increases accuracy butwastes lots of data).

We note that as we lower our noise threshold, there are some-times small peaks that buck the general trend. This is because ourthresholding method occasionally cuts out some data that givesgood results, causing a small rise in our error. We observe that atabout 41% of data from the optimal sensor kept across all subjects,the error starts to rise. Setting the threshold to the ICDF functionfor 89% minimizes this error.

3.2.2 Denoising for Engine Noise Reduction. We then do denois-ing to further reduce the noise in the signal. We place one sensornear the bottom of the backrest, away from the heart, to charac-terize noise. We observed that the level of noise recorded by eachsensor is different, so it works best to partially subtract one sen-sor’s signal from the other. We multiply the noise sensor signal bya fraction (we obtained 20% heuristically) and subtract it from therest of the sensors.

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Figure 4: The mean absolute RR-interval error with differ-ent wavelet scales, (a), with characterization data, and (b)with human subject data. Using a scale of 200minimized ourerror.

3.2.3 Peak Emphasis to Enhance Heartbeat Signal. In order toenhance the weak peaks of the heartbeat signal, we do root-mean-square averaging, which preserves the peaks in the data and smoothsthe high-frequency noise.

3.2.4 The Continuous Wavelet Transform for Heartbeat Isolation.To further reduce the noise, we isolate the periodic nature of theheartbeat using a continuous wavelet transform (CWT )[1]. TheCWT compares the signal to compressed and stretched versions ofa wavelet, theCWT ’s analyzing function. The Mexican hat wavelethas been widely used for characterizing impulse signals, which fitsour target signal profiling. It is described by

ψ (t) =2

√3π

14(1 − t2)e

−t22 (1)

The stretching and compressing of the wavelet performed bythe CWT is known as "scaling": the CWT is a function of scale (a)and position (b):

C(a,b; f (t),ψ (t)) =∫ ∞

−∞

f (t)1aψ (

t − b

a)dt (2)

f (t) is the signal, t is time, andψ (t) is the wavelet function.By varying the values of the scale parameter, a, and the position

parameter, b, we obtain the cwt coefficients C(a,b). We chose thescale that best represents our data and varied the position coeffi-cients to obtain a one-dimensional filtered signal. Figure 4 showsthe result of the wavelet transform using scales between 1 and 700.VVRRM chose the scale that gives the lowest error, in this case,scale 200. Scale 200 corresponds to a frequency of 2.5 Hz, which,at 150 beats per minute, is higher than any heart rate we expectto see. This makes sense because the heartbeat is made up of sev-eral components, described in the background section. Choosingtoo low a frequency would lose information from these differentcomponents, while too high a frequency wouldn’t filter out enginenoise.

3.3 RR-Interval EstimationOnce the signal is enhanced and filtered, we calculate RR-intervalwith the sensor with the highest signal amplitude.

3.3.1 Sensor Selection. To adapt to a wide range of heart posi-tions with sparse sensors, we displace the sensors with minimumoverlapping sensing range and choose the sensor closest to theheart. In each window, we calculate the mean of the wavelet co-efficients for each sensor, and select the one with highest mean,indicating highest Signal-to-Noise Ratio. This algorithm dependson our having removed noisy parts in the signal and reduced thenoise in the remaining signal, as high amounts of noise could alsocause a higher mean, and cause the incorrect selection of a sensoras "optimal".

3.3.2 Peak Detection for Peaks of Heartbeat Motion. For thefirst step in our peak detection algorithm, we do root-mean-squareaveraging of the wavelet coefficients, which smooths the data, lim-iting small false peaks and emphasizing larger peaks. We find localmaxima by calculating the derivative of our signal in two pointsin time and comparing them to see if the difference lies above agiven threshold [10]. Then we pick points near the local maximaand apply Least Squares Curve Fitting over them to refine the peaklocation [10].

3.3.3 RR-Interval Estimation. Weoccasionally detect extra peaksor drifted peaks from noise in the signal, which we remove to getaccurate RR-intervals. Since heartbeats occur at periodic intervals,we discard the lowest magnitude peak of any pair of peaks that arecloser than 220 beats per minute, which we take as our maximumheart rate (well above the normal resting heart rate of 60-100 beatsper minute). This will not affect HRV monitoring in healthy sub-jects, because while heart rate does oscillate over time, it is not soirregular that it would have beats this close together and maintaina normal heart rate. Then we find the distance in time betweenthe first two peaks in our sliding window and consider that ourRR-interval. Now that we have our RR-interval, we take our nextwindow of data starting at a location just past the 1st peak, ensuringthat we don’t miss any heartbeats as we move the window forward.

4 EXPERIMENTATION ANDCHARACTERIZATION

We evaluated our system with two sets of experiments: controlledheartbeat characterization input, and uncontrolled human subjectinput.

4.1 Experimental SetupWe use the W354C03_010G10 piezoelectric accelerometer, sampledat 2 kHz [12]. We chose to put the sensors on the passenger seatbecause passengers move more freely and would give us morevaried motion noise. We plan to test the system with steering andother drivingmotions in a future driving scenario.We put sensors atdifferent heights on the backrest to accommodate different heightsof drivers and passengers. The sensor layout is shown in Figure 5.For the characterization experiments, we have three sensors atdifferent heights on the backrest (sensors 1, 2, and 3), while for thehuman subject experiments we put two sensors on the backrestat different heights and one sensor on the seat belt across the lap(sensors 1, 2, and 5). This is so that if the person leans forward, wecan still pick up the motion of the heartbeat through their stomach.Sensor 4, the noise sensor, helps us characterize and remove the

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Figure 5: Hardware setup. Sensor 3 was only in characteriza-tion experiments, while sensor 5 was only for human sub-ject experiments. Sensor 4 measured noise.

noise in the system. For the characterization experiments, we use asmall set of speakers as the input that lean against the seat and playthe sound of a heart beating. This allows us to control the heightand rate of the heart. For the human subject experiments, we havehuman subjects sit in the seat, wearing a Zephyr Bioharness belt(capacitive ECG) as the ground truth [15].

4.2 Heartbeat CharacterizationIn our characterization experiments, we tested different heightsand rates of the heart, and different car engine speeds. In order tocontrol heartbeat characteristics in a realistic setting, in our heart-beat characterization experiments, we manufactured a heartbeatsignal by putting a set of speakers against the backrest of the carseat, and playing the sound of heartbeats at different rates and withthe speakers in different positions against the seat.

To test the effect of heights, we put the simulated heart at 7different heights against the backrest of the car seat and playedheartbeat sounds at 60 BPM. The results are shown in Figure 6(a).Using a single sensor, as the simulated heart got further from thesensor the error increased, but with our sensor selection algorithm,the sensor closest to the heartbeat was consistently chosen and thaterror was minimized.

4.2.1 Different rates. Figure 6(b) shows the accuracy for differ-ent heart rates. In this experiment, we kept the simulated heart inthe same place and changed the rate of the heartbeat signal. Wefound that while there was a small difference in error, with lowerheart rates having a slightly larger error, our system performedwell across the range. This tells us that our algorithm is not affectedby heart rate.

4.2.2 Engine noise evaluation. Figure 6(c) shows the result withthe engine revving at different speeds with the car in neutral.We seethat different engine speeds did not greatly affect our error. This

Figure 6: In (a), we compare the sensor selection algorithmwith using a single sensor, and see that using sensor selec-tion lowers the average error compared to using a singlesensor. In (b), we see that our algorithm is not significantlyaffected by heart rate. In (c), we see that different enginespeeds do not greatly affect error.

shows the continuous wavelet filter effectively removed enginenoise.

Overall the mean absolute error for our characterization experi-ments was 3 ms, and we accurately categorized 100% of our datato within 100 ms of the ground truth (The error of RR-intervalsshould be lower than 100ms [6]).

4.3 Human Subject ExperimentsFor our human subject experiments, we asked 4 subjects, 3 men and1 woman, to sit in an idling car. The participants were between theages of 25 and 42. We collected 4 minutes of data from each of themfor a total of 16 minutes. We noted the time when talking, laughing,coughing, gesturing or shifting positions happened, and found thatlaughing and coughing caused the most obvious noise in the data.Figure 7 shows the results of the human subject experiments. Fromthis figure, we can see that the system is able to effectively recognizeand ignore these noisy parts of the data by fitting a distributionto the data and introducing a threshold, as described in Section 3.When we look at the data by subject, we can see that there is somevariation in the data. This can partly be explained by the way thesubjects behaved. The extent to which the different subjects talked,laughed, coughed and shifted positions is reflected in the error rateand percent of data under the noise threshold for each subject.

Subject 1 watched funny videos while sitting in the car, and hisfrequent laughter caused significant noise in the data. However,data with laughing noise was effectively ignored by our algorithm,which greatly reduced both the amount of his data that we usedand the error associated with it.

Subject 2 talked frequently and fidgeted. The thresholding al-gorithm also discarded a lot of her data, but it didn’t reduce the

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Figure 7: Graph (a) shows the 25% and 75% confidence in-tervals of the RR-interval absolute error for each humansubject. The circles mark the mean error and the horizontallines mark the median error. (b) shows how much data foreach human subject was kept before and after the threshold-ing algorithm. One can see that there is variation per personin the amount of data under the noise threshold and the er-ror.

error by as much. This could be because some of her periodic fid-geting was confused with heart motion. Subjects 1, 3 and 4 wereall between 177 and 180 cm, while subject 2 was 167 cm, signif-icantly shorter than the other subjects. Additionally, the sensorselected for Subject 2 was the one across the lap, while for the othersubjects it was a sensor in the backrest. This suggests that eitherSubject 2 spent a lot of time leaning forward, or the sensors in thebackrest were in a bad location to pick up heart rate for shortersubjects. In the future, more subjects with varying heights will helpus determine this.

Subject 3 also talked during most of the data collection, withoccasional coughing or laughing. This behavior occurred less thansubject 1, so less of his data was discarded.

Subject 4 preferred to sit quietly and meditate, so his data hadmuch less motion noise and the lowest error. Subject 4’s results sug-gest that keeping as much data as possible to acquire RR-intervalsis a crucial component of obtaining accurate HRV measurements.

Overall the mean absolute error for RR-intervals was 54 msacross all subjects. 84% of our data under threshold was accuratelycategorized to within the 100 ms error defined in medical litera-ture [6].

5 CONCLUSIONIn this paper, we introduced VVRRM , a real-time system that usesambient car seat vibration to monitor the individual heartbeats ofa subject in a car. VVRRM characterizes and removes the motionnoise caused by an idling car and the movement of people, andpresents an adaptive sensing algorithm to detect the heartbeatsignal in the midst of high levels of noise. Our evaluation showsthat we can accurately estimate RR-intervals with the high levelsof noise present in an automobile, with on average 54 ms meanabsolute error for human subjects and 3 ms mean absolute errorfor characterization experiments, significantly lower than the 100ms requirement for RR intervals. Work is still needed to expand

characterization and further noise removal in multiple highwaydriving scenarios and with more human subjects.

ACKNOWLEDGEMENTSThis research was supported in part by the National Science Foun-dation (under grants CNS-1149611, CMMI-1653550, CNS-1404118,and CNS-1423020), the National Science Foundation Graduate Re-search Fellowship Program (under Grant No. DGE 1745016), Inteland Google. The views and conclusions contained here are those ofthe authors and should not be interpreted as necessarily represent-ing the official policies or endorsements, either express or implied,of Carnegie Mellon University, the National Science Foundation, orthe U.S. Government or any of its agencies.

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