Unobtrusive vital sign detection through ambient physical vibrations
Creators: name: Jia, Zhenhua
Publisher: name: No Publisher Supplied
Year: 2019

description: Vital sign monitoring is critically important to ensuring the well-being of many people, ranging from patients to the elderly. Technologies that support vital sign monitoring should be unobtrusive, and solutions that are accurate and can be easily applied to existing beds is an important need that has been unfulfilled. In this dissertation, we aim at tackling the challenge of accurate, low-cost and easy to deploy vital sign monitoring. We focus on two scenarios ones everyday life – sleeping during the night and sitting during the daytime, considering that a person spends a large portion of time on both activities.In the first part of this dissertation, we investigate whether off-the-shelf analog geophone sensors can be used to detect heartbeats when installed under a bed. Geophones have the desirable property of being insensitive to lower-frequency movements, which lends itself to heartbeat monitoring as the heartbeat signal has harmonic frequencies that are easily captured by the geophone. With carefully-designed signal processing algorithms, we show it is possible to detect and extract heartbeats in the presence of environmental noise and other body movements a person may have during sleep. We built a prototype sensor and conducted detailed experiments involving 43 subjects, which demonstrate that the geophone sensor is a compelling solution to long-term, at-home heartbeat monitoring. We compared the average heartbeat rate estimated by our prototype and that reported by a pulse oximeter. The results revealed that the average error rate is around 1.30% over 500 data samples when the subjects were still on thebed, and 3.87% over 300 data samples when the subjects had different types of body movements while lying on the bed. We also deployed the prototype in the homes of 9 subjects for a total of 25 nights, and found that the average estimation error rate was 8.25% over more than 181 hours’ data.n the second part of this dissertation, we greatly extend our previous system towards a more realistic scenario. We develop a system, called VitalMon, aiming to monitor a person’s respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in the amplitude fluctuation of the heartbeat signal. We have developed a prototype bed to evaluate the proposed algorithms. A total of 86 subjects participated in our study, and we collected 5084 geophone samples, totaling 56 hours of data. We show that our technique is accurate – its breathing rate estimation error for a single person is 0.38 breaths per minute (median error is 0.22 breaths per minute), heart rate estimation error when two persons share a bed is 1.90 beats per minute (median error is 0.72 beats per minute), and breathing rate estimation error when two persons share a bed is 2.62 breaths per minute (median error is 1.95 breaths per minute). By varying the sleeping posture and mattress type, we show that our system can work in many different scenarios.In the third part of this dissertation, we introduce a system, called Touch-Chair, which unobtrusively monitors a user’s respiration and learns a user’s identity through capacitive sensing. Touch-Chair consists of 16 capacitive sensors mounted on the surface of a chair. The system can easily detect any occupancy event and extract the unique micro details about the user’s respiration and sitting behavior patterns, through signal processing and supervised machine learning techniques. Our system can provide fine-grained information towards better understanding a user’s health state.