2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks
Demo Abstract: HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System Zhenhua Jia
Richard E. Howard
Wireless Information Network Laboratory Rutgers University, USA
Wireless Information Network Laboratory Rutgers University, USA
Yanyong Zhang
Pei Zhang
Wireless Information Network Laboratory Rutgers University, USA
Department of Electrical and Computer Engineering Carnegie Mellon University, USA
ABSTRACT
wristbands or smart watches. However, such a device has many limitations. It commonly requires to be bundled with a mobile phone and its battery needs to be charged frequently. Other systems can be quite cumbersome since they may require custom-made parts, such as special designed bed sheet, mattress, etc. Overall, few systems are accurate, unobtrusive, robust and easy to install at the same time. As such, even though many systems have been proposed in the past, heartbeat monitoring during sleep at home and at a nursing facility is still a not completely solved problem. In this work, we propose a system for monitoring heartbeats during sleep through sensing the ballistic vibrations generated by each heartbeat. In particular, we use a commercial off-the-shelf geophone [1] mounted on a bed to detect users’ heartbeats while sleeping. We call our system HB-Phone in short since it’s just like a phone to ”hear” the sound of heartbeats. Compare to other systems, our system brings several advantages. First, geophone sensors are quite sensitive, even for tiny vibrations like the heartbeat vibrations propagating through a bed. Second, geophone sensors are commercially available and the entire system is affordable. Third, installing the system is very convenient. A user can easily insert the system between the mattress and the frame of the bed without introducing additional interference. In our study, we demonstrate the system is robust and accurate against noise from the environment and gross body motions. The rest of this paper is organized as follows. We describe HBPhone briefly in Section 2. In Section 3, we present our experimental results. Finally, we explain our demonstration plan in Section 4. We refer the reader to our full paper [4] in IPSN 2016 for more details on the system design and evaluation.
Monitoring heartbeats takes an important role to ensure a person’s health and well-being. Few of the existing systems are accurate, unobtrusive, robust and easy to install at the same time. Thus, we propose a completely unobtrusive system which can detect heartbeats during sleep by sensing the weak ballistic vibrations caused by heartbeats on any bed. The system, HB-Phone, is centered around the off-the-shelf geophone sensor and can be easily installed on an existing bed. In this demo, we demonstrate that our system can detect and extract heartbeats accurately and in real time, even with the presence of noise from the environment and gross body movements during sleep.
CCS CONCEPTS •Hardware →Digital signal processing; Sensor applications and deployments;
KEYWORDS Heartbeat Sensing, Bed-Mounted Sensor, Sleep Monitoring, Signal Processing ACM Reference format: Zhenhua Jia, Richard E. Howard, Yanyong Zhang, and Pei Zhang. 2017. Demo Abstract: HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System. In Proceedings of The 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, Pittsburgh, PA USA, April 2017 (IPSN 2017), 2 pages. DOI: http://dx.doi.org/10.1145/3055031.3055042
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INTRODUCTION
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Heartbeat monitoring is important to ensure people’s health and well-being. However, a reliable system which can monitor heartbeats during sleep at home and at a nurse facility is still missing. Quite a few systems have been proposed in the last few years. One of the common approaches is using wearable devices, such as
SYSTEM
We design HB-Phone as a heartbeat monitoring system during sleep for patient care and elderly care at home and at a nursing facility. The system overview of HB-Phone is shown in Figure 1. HB-Phone leverages a commercial off-the-shelf geophone sensor. A geophone sensor has a spring-mounted magnet mass moving within a wire coil to generate a voltage. It measures the speed of vibrations at different frequencies. We simply mount HB-Phone between the mattress and the frame to capture slight vibrations propagating through the bed, including the user’s heartbeats. Then, we amplify the weak analog signal from the geophone and convert it to a digital signal. Next, we apply a series of signal processing techniques to extract the heartbeats.
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[email protected]. IPSN 2017, Pittsburgh, PA USA © 2017 ACM. 978-1-4503-4890-4/17/04. . . $15.00 DOI: http://dx.doi.org/10.1145/3055031.3055042
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IPSN 2017, April 2017, Pittsburgh, PA USA
Zhenhua Jia, Richard E. Howard, Yanyong Zhang, and Pei Zhang
Figure 1: Overview of the HB-Phone system. A geophone sensor is placed between the mattress and the frame. It generates a weak electrical signal while capturing the vibrations from the heart and other sources. The signal gets amplified and converted into a digital signal. Then, a series of signal processing techniques are applied to extract the heartbeats. The signal processing part is done as follows. First, we apply a low-pass filter to remove the majority of noise from the environment and gross body motions. Second, we compute the power of the signal in time domain to reduce the impact of phase lag among multiple heartbeats. Third, we calculate sample auto-correlation function (sample ACF) [3] of the signal power. Fourth, we apply a Peak Finding and Measurement algorithm [2] to the sample ACF data. Finally, we extract the periodicity of the heartbeat signal and detect the heartbeats. We choose this method because (i) we observe that it is possible to separate heartbeat signals from body movement signals by filtering, and (ii) heartbeats exhibit strong periodicity compared to most other body movements. Please note that geophone is very insensitive to respiration – another common periodic motion – due to its lower frequency.
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average error rate is about 3.9%. For the real-world deployment at 9 subjects’ rooms, the results shows the average error rate is about 8.3% over 180 hours’ data in 25 nights. Overall, HB-Phone shows its robustness against many types of gross body motions during sleep and its potential of monitoring heartbeats at home and at nursing facilities.
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DEMONSTRATION
We will show our demo in two steps. First, we will show a video of detecting the heart rate in real time while a subject lays on a bed in a lab environment. The video will also show the raw vibration signal, the estimated heart rate, and the ground truth simultaneously. During the interactive session, we will set up our system with a display. People can try our system. The output of the system will include the raw signal and the estimated heart rate.
EVALUATION
REFERENCES
We evaluate the performance of HB-Phone by comparing the estimated heart rate against the ground truth collected by a medical grade pulse oximeter. The evaluation consists of two phases: controlled experiments in a lab environment and real-world deployments at subjects’ houses. For the controlled experiments, we repeat more than 500 30-second experiments from 34 subjects when the subjects lay still on a bed. The average error rate is about 1.3%. Next, we repeat more than 300 30-second experiments when the subjects have different body motions while lying on the bed. The
[1] 2017. Geophone SM-24. https://www.sparkfun.com/products/11744. [2] 2017. Peak Finding and Measurement. http://terpconnect.umd.edu/∼toh/spectrum/ PeakFindingandMeasurement.htm. [3] Jenkins Box and Gwilym M Jenkins. 1994. Reinsel. Time Series Analysis, Forecasting and Control. Prentice Hall, Englewood Cliffs, NJ, USA, 3rd edition edition. [4] Zhenhua Jia, Musaab Alaziz, Xiang Chi, Richard E Howard, Yanyong Zhang, Pei Zhang, Wade Trappe, Anand Sivasubramaniam, and Ning An. 2016. HB-Phone: A Bed-Mounted Geophone-Based Heartbeat Monitoring System. In 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 1–12.
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