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Recent studies have shown that head movements associated with cardiac activity contain a heart rate (HR) signal. In most previous studies, subjects were required to remain stationary in a specific environment during HR measurements, and measurement accuracy depended on the choice of target in the scene, i.e., the specified region of the face. In this paper, we proposed a robust HR measurement method based on ballistocardiogram (BCG) technology. This method requires only a camera and does not require that users establish a complex measurement environment. In addition, a bidirectional optical flow algorithm is designed to select and track valid feature points in the video captured by using the camera. Experiments with 11 subjects show that the HR values measured using the proposed method differ slightly from the reference values, and the average error is only 1.09%. Overall, this method can improve the accuracy of BCG without limitations related to skin tone, illumination, the state of the subject, or the test location.

This study demonstrates the feasibility of using a seat sensor designed for occupant classification from a production passenger vehicle to measure an occupant’s respiration rate (RR) and heart rate (HR) in a laboratory setting. Relaying occupant vital signs after a crash could improve emergency response by adding a direct measure of the occupant state to an Advanced Automatic Collision Notification (AACN) system. Data was collected from eleven participants with body weights ranging from 42 to 91 kg using a Ford Mustang passenger seat and seat sensor. Using a ballistocardiography (BCG) approach, the data was processed by time domain filtering and frequency domain analysis using the fast Fourier transform to yield RR and HR in a 1-min sliding window. Resting rates over the 30-min data collection and continuous RR and HR signals were compared to laboratory physiological instruments using the Bland-Altman approach. Differences between the seat sensor and reference sensor were within 5 breaths per minute for resting RR and within 15 beats per minute for resting HR. The time series comparisons for RR and HR were promising with the frequency analysis technique outperforming the peak detection technique. However, future work is necessary for more accurate and reliable real-time monitoring of RR and HR outside the laboratory setting., Competing Interests: The authors declare no conflict of interest.

Ballistocardiography (BCG) measures vibrations of the body caused by ejection of blood from the heart, and the root mean square (RMS) of BCG measured with a weighing scale trends with cardiac output. However, BCG underwater has not been studied. Head-to-foot BCG signals were recorded with an accelerometer on the sternum of three human subjects. The heartbeats were clearly visible in the signals recorded underwater, and the resting change in RMS BCG was +360 μg (+36%) from air to cold water immersion (27.8 °C) while standing. This is within the 32%-62% increase in cardiac output observed in previous head-out immersion studies.

We propose a simple and robust method to detect heartbeats using the ballistocardiogram (BCG) signal that is produced by a hydraulic bed sensor placed under the mattress. The proposed method is found beneficial especially when the BCG signal does not display consistent J-peaks, which can often be the case for overnight, in-home monitoring, especially with frail seniors. Heartbeat detection is based on the short-time energy of the BCG signal. Compared with previous methods that rely on the J-peaks observed from the BCG amplitude, we are able to achieve considerable improvement even when significant distortions are present. Test results are included for different BCG waveform patterns from older adults.

The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results (p < 0.05).

Seismocardiogram (SCG) is the measure of the micro-vibrations produced by the heart contraction and blood ejection into the vascular tree. Over time, a large body of evidence has been collected on the ability of SCG to reflect cardiac mechanical events such as opening and closure of mitral and aortic valves, atrial filling and point of maximal aortic blood ejection. We recently developed a smart garment, named MagIC-SCG, that allows the monitoring of SCG, electrocardiogram (ECG) and respiration out of the laboratory setting in ambulant subjects. The present pilot study illustrates the results of two different experiments performed to obtain a first evaluation on whether a dynamical assessment of indexes of cardiac mechanics can be obtained from SCG recordings obtained by MagIC-SCG. In the first experiment, we evaluated the consistency of the estimates of two indexes of cardiac contractility, the pre-ejection period, PEP, and the left ventricular ejection time, LVET. This was done in the lab, by reproducing an experimental protocol well known in literature, so that our measures derived from SCG could have been compared with PEP and LVET reference values obtained by traditional techniques. Six healthy subjects worn MagIC-SCG while assuming two different postures (supine and standing); PEP was estimated as the time interval between the Q wave in ECG and the SCG wave corresponding to the opening of aortic valve; LVET was the time interval between the SCG waves corresponding to the opening and closure of the aortic valve. The shift from supine to standing posture produced a significant increase in PEP and PEP/LVET ratio, a reduction in LVET and a concomitant rise in the LF/HF ratio in the RR interval (RRI) power spectrum. These results are in line with data available in literature thus providing a first support to the validity of our estimates. In the second experiment, we evaluated in one subject the feasibility of the beat-by-beat assessment of LVET during spontaneous behavior. The subject was continuously monitored by the smart garment from 8 am to 8 pm during a workday. From the whole recording, three data segments were selected: while the subject was traveling to work (M1), during work in the office (O) and while traveling back home (M2). LVET was estimated on a beat-by-beat basis from SCG and the RRI influence was removed by regression analysis. The LVET series displayed marked beat-by-beat fluctuations at the respiratory frequency. The amplitude of these fluctuations changed in the three periods and was lower when the LF/HF RRI power ratio was higher, at O, thus suggesting a possible influence of the autonomic nervous system on LVET short-term variability. To the best of our knowledge this case report provides for the first time a representation of the beat-by-beat dynamics of a systolic time interval during daily activity. The statistical characterization of these findings remains to be explored on a larger population., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Encouraged by previous performance of a hydraulic bed sensor, this work presents a new hydraulic transducer configuration which improves the system's ability to capture a heartbeat signal from four subjects with different body weight and height, gender, age and cardiac history. It also proposes a new approach for detecting the occurrence of heartbeats from ballistocardiogram (BCG) signals through the use of the k-means clustering algorithm, based on finding the location of the J-peaks. Preliminary testing showed that the new transducer arrangement was able to capture the occurrence of heartbeats for all the participants, and the clustering approach achieved correct heartbeat detection ranging from 98.6 to 100% for three of them. Some considerations are discussed regarding adjustments that can be done in order to increase the correct detection of heartbeats for the participant whose percentage of correct detection ranged from 71.0 to 92.5%.

Traditionally, ballistocardiogram (BCG) has been measured using large and stationary devices. In this work, we demonstrate a portable and continuous BCG monitor that is wearable at the ear. The device has the form factor of a hearing aid and is wirelessly connected to a PC for data recording and analysis. With the ear as an anchoring point, the device uses a MEMS tri-axial accelerometer to measure BCG at the head. Morphological differences exist between head BCG and traditional BCG, but the principal peaks (J waves) and their vectors are preserved. The frequency of J waves corresponds to heart rate, and when used in conjunction with an electrocardiogram's (ECG) R wave, the timing of J waves yields the RJ interval. Results from our clinical study show linear correlation between the RJ interval and the heart's pre-ejection period during hemodynamic maneuvers, thus revealing important information about cardiac contractility and its regulation.

Sternal seismocardiogram (SCG) is the assessment of microvibrations produced by the beating heart as detected by an accelerometer positioned on the sternum. This signal reflects mechanical events of the heart contraction, including the opening and closure of mitral and aortic valves and maximal blood flow acceleration. Traditionally, SCG has been detected in a laboratory setting with the subject lying at rest in supine position. Aims of this study were 1) to investigate the feasibility of a SCG monitoring over the 24 hours in ambulant subjects, and 2) to calculate number and time distribution of the SCG estimates obtainable over the 24 hours. In 5 healthy subjects ECG, respiration, body accelerations and sternal SCG were recorded for 24 hours in a workday by a smart garment recently developed in our laboratory, the MagIC-SCG system. Each recording was split into a series of contiguous 5-s data segments and SCG was estimated in each segment where the magnitude of the acceleration vector was < 4 milli-g (this condition indicates that the subject was not moving).All the 24-h recordings were found of good quality and could be entirely analyzed. A large number of SCG estimates could be obtained over the 24 hours. In particular, more than 100 estimates per hour were available during the day; at night this rate was three times higher.Thus our study indicates that not only the 24h SCG monitoring in daily life is feasible but also that possible changes over time in SCG and its derived parameters may be tracked with an extreme temporal detail.

Using the Ballistocardiogram(BCG) measured on weighing scale, heart rate variability(HRV) and blood pressure were estimated. BCG was measured while subjects were on weighing scale in resting state and under the Valsalva maneuver and static exercise condition to induce the change in cardiac autonomic rhythm. Time domain, frequency domain and nonlinear HRV parameters were estimated from the measured BCG and compared with the ones calculated from ECG measured simultaneously. For blood pressure(BP) estimation, ECG was measured additionally on the feet using dry electrodes simultaneously installed on weighing scale and R-J intervals were extracted as a BP correlated parameter at every beat cycle. HRV estimation results shows the correlation higher than 0.97, and the estimated BP was similar to the measured BP with a reliable correlations.

Our work covers improvements in sensors and signal processing for unobtrusive, long-term monitoring of cardiac (and respiratory) rhythms using only non-invasive vibration sensors. We describe a system for the unobtrusive monitoring of vital signs by means of an array of novel optical ballistocardiography (BCG) sensors placed underneath a regular bed mattress. Furthermore, we analyze the systems spatial sensitivity and present proof-of-concept results comparing our system to a more conventional BCG system based on a single electromechanical-film (EMFi) sensor. Our preliminary results suggest that the proposed optical multi-channel system could have the potential to reduce beat-to-beat heart rate estimation errors, as well as enable the analysis of more complex breathing patterns.

Ubiquitous medical technology may provide advanced utility for evaluating the status of the patient beyond the clinical environment. The iPhone provides the capacity to measure the heart rate, as the iPhone consists of a 3-axis accelerometer that is sufficiently sensitive to perceive tiny body movements caused by heart pumping. In this preliminary study, an iPhone was tested and evaluated as the reliable heart rate extractor to use for medical purpose by comparing with reference electrocardiogram. By comparing the extracted heart rate from acquired acceleration data with the extracted one from ECG reference signal, iPhone functioning as the reliable heart rate extractor has demonstrated sufficient accuracy and consistency.

In this paper we propose a new simple method to measure the heart rate of a person sitting on a chair or lying in a bed. The heart rate is measured with a thin sensor pad consisting of separate electromechanical film (EMFi) and polyvinylidenefluoride (PVDF) transducers located beneath the leg of chair or bed. This study aims to evaluate the operation of the sensor system with measurements, and also to compare the results provided by the two transducer materials. Based on the results obtained here, the heart rates measured with the transducers mainly corresponded to the values of reference ECG signal. Some minor differences between the heart rate values of PVDF and EMFi appeared, especially in supine position, possible due to the material sensitivities to different force directions. However, to conclude, both materials seem to be convenient for this kind of measurement of heart rate.

This study analyses the spectral components of the heart rate fluctuations of a new contact-less technology for sleep evaluation. Both heart beat interval (HBI) and movement activity were extracted from the multichannel ballistocardiographic (BCG) measurements, based on Emfit sensor foils placed into bed mattress. Powers spectral densities (PSD) of HBI have been compared with the ones obtained from the standard ECG during sleep stage 2. In addition, spectral features obtained from the contact-less technology and standard ECG has been used to automatically classify the sleep macrostructure through a time-varying autoregressive model and a Hidden Markov Model. Whole night recordings from six subjects were analyzed in this study. Spectral components did not show significant differences between the two measurements. Further, contactless technology achieved a total accuracy of 83 % and kappa index of 0.42, while standard ECG achieved an accuracy of 84 % and kappa index of 0.43 when compared to clinical sleep staging from polysomnography.

We propose a novel technique for beat-to-beat heart rate detection based on the ballistocardiographic (BCG) force signal from a subject standing on a common electronic weighing scale. The detection relies on sensing force variations related to the blood acceleration in the aorta, works even if wearing footwear and does not require any sensors attached to the body because it uses the load cells in the scale. We have devised an approach to estimate the sensitivity and frequency response of three commercial weighing scales to assess their capability to detect the BCG force signal. Static sensitivities ranged from 490 nV V(-1) N(-1) to 1670 nV V(-1) N(-1). The frequency response depended on the subject's mass but it was broad enough for heart rate estimation. We have designed an electronic pulse detection system based on off-the-shelf integrated circuits to sense heart-beat-related force variations of about 0.24 N. The signal-to-noise ratio of the main peaks of the force signal detected was higher than 30 dB. A Bland-Altman plot was used to compare the RR time intervals estimated from the ECG and BCG force signals for 17 volunteers. The error was +/-21 ms, which makes the proposed technique suitable for short-term monitoring of the heart rate.

In this paper a novel system for detection of the mechanical movement of heart, mechanocardiography (MCG), with no connection to the subject's body is presented. This signal is based on radar technology. The acquired signal is highly correlated to the acceleration-based ballistocardiograph signal (BCG) recorded directly from the sternum. It is shown that the heart and breathing rates can be reliably detected using this system.

We propose a novel technique for heart rate detection on a subject that stands on a common electronic weighing scale. The detection relies on sensing force variations related to the blood acceleration in the aorta, works even if wearing footwear, and does not require any sensors attached to the body. We have applied our method to three different weighing scales, and estimated whether their sensitivity and frequency response suited heart rate detection. Scale sensitivities were from 490 nV/V/N to 1670 nV/V/N, all had an underdamped transient response and their dynamic gain error was below 19% at 10 Hz, which are acceptable values for heart rate estimation. We also designed a pulse detection system based on off-the-shelf integrated circuits, whose gain was about 70x10(3) and able to sense force variations about 240 mN. The signal-to-noise ratio (SNR) of the main peaks of the pulse signal detected was higher than 48 dB, which is large enough to estimate the heart rate by simple signal processing methods. To validate the method, the ECG and the force signal were simultaneously recorded on 12 volunteers. The maximal error obtained from heart rates determined from these two signals was +/-0.6 beats/minute.

A multichannel pressure sensing Emfit foil was integrated to a bed mattress for measuring ballistocardiograph signals during sleep. We calculated the heart beat interval with cepstrum method, by applying FFT for short time windows including pair of consequent heart beats. We decreased the variance of FFT by averaging the multichannel data in the frequency domain. Relative error of our method in reference to electrocardiograph RR interval was only 0.35% for 15 night recordings with six normal subjects, when 12% of data was automatically removed due to movement artifacts. Background motivation for this work is given from the studies applying heart rate variability for the sleep staging.

Polysomnography is the standard method to score the sleep stages or to determine the quality of sleep. During all night examination, subjects must attach complicate and numerous electrodes on their body to acquire the biosignals. They can influence participants' sleep stage transition or sleep pattern, and the method seems intricate. In many researches, it is reported that autonomic nervous system (ANS) is varying with sleep stage transition and heart rate variability (HRV) is one of the indices which reflects the changes of autonomic nervous system. In this point of view, we can estimate the sleep quality by observing the HRV variation. In this study, to analyze the heart rate variability, we introduce a new system that can detect the heart beats of subjects during sleep by using bed installed load-cell sensors. The pressure to the sensor changes with the pulsation of the heart and we consider it as ballistocardiogram, the physical heart beat signal. To validate our system, we adopted this system for the 4 subjects with the polysomnography. The results show the LF/HF ratio of the heart rate, one of the reflection parameters of ANS, acquired from the system for each sleep stage. To validate the results, the HRV from electrocardiogram using Ag-AgCl electrodes will be compared.

We describe a noncontact method for the ambulant measurement of basic sleep physiology parameters in humans, particularly for field studies involving sleep research and sleep disturbances. This method traces the body movements, respiration, and heart action of a person at rest or asleep on a bed, using four high-resolution force sensors installed under the bedposts. The recoil movement of the body at each heartbeat, known as the cardioballistic effect, as well as the lifting and lowering of the thorax, while breathing, causes very small shifts o f the center of gravity of the bed andthe subject. These shifts arereflected in the altering forcedistributions across the four sensors. Cardiac and respiratory parameters and the subject's movement activity can be calculated from the sensor signals. Neither electrodes nor other kinds of transducers are in direct contact with the subject, which is the main advantage of this technique over conventional methods. Laboratory experiments were carried out to estimate validity and practicability. The method has been found to be adequate, especially for automated and unattended sleep-data collection over long periods of time.