REVIEW PAPER 1

Ballistocardiogram Signal Processing: A Literature Review Ibrahim Sadek, Member, IEEE

Abstract—There are several algorithms for analyzing and information about the overall performance of the circulatory interpreting cardiorespiratory signals obtained from in-bed based system; this is because BCG measures the mass movements, sensors. In sum, these algorithms can be broadly grouped i.e., the mass of the circulating blood and the heart during into three categories: time-domain algorithms, frequency-domain algorithms, and wavelet-domain algorithms. A summary of the cardiac cycle [1]. During atrial systole, when the blood is these algorithms is given below to highlight which category ejected into the large vessels, the center of mass of the body of algorithms will be used in our analysis. First, time-domain moves towards the head of the body. In other ways, when the algorithms are mainly focused on detecting local maxima or blood moves towards the peripheral vessels and concentrates local minima using a moving window, and therefore finding further away from the heart in the peripheral vessels, the center- the interval between the dominant J-peaks of ballistocardiogram signal. However, this approach has many limitations because of of-mass moves towards the feet (Fig. 1(b)). This shift comprises the nonlinear and nonstationary behavior of the ballistocardio- several components as a result of cardiac activity, respiration, gram signal. The implication is that the ballistocardiogram signal and body movements. This shifting of the center of mass of the does not display consistent J-peaks, which can usually be the body generates the BCG waveform since the blood distribution case for overnight, in-home monitoring, particularly with frail changes during the cardiac cycle [5]. More than 100 years ago, elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do BCG failed to prove its functionality, and it did not start to be not provide information about interbeat intervals. Nevertheless, used in routine tasks for a few general reasons as follows. First, they can provide information about heart rate variability. This is there had been insufficient standard measurement methods, i.e., usually done by taking the fast Fourier transform or the inverse different methods had resulted in slightly different signals. Fourier transform of the logarithm of the estimated spectrum, Second, the exact physiologic origin of the BCG waveform i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency had not been well-understood. Furthermore, there had been range. The limit of these algorithms is that the peak in the insufficient clear guidelines for interpretation of the results, spectrum may get wider and multiple peaks may appear, which and therefore the medical community was unwilling to take might cause a problem in measuring the vital signs. At last, risks. Third, there had been a dominant focus on some clinical the objective of wavelet-domain algorithms is to decompose the diagnostic, for example, myocardial infarction, angina pectoris, signal into different components, hence the component which shows an agreement with the vital signs can be selected. In coronary heart disease; these applications need a high level other words, the selected component contains only information of specificity and reliability that the BCG had not reached. about the heart cycles or respiratory cycles, respectively. Inter- Fourth, the emergence of ultrasound and echocardiography beat intervals can be found easily by applying a simple peak methods that swiftly overhauled BCG and related methods for detector. An empirical mode decomposition is an alternative noninvasive cardiac and hemodynamic diagnostic [6]. approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such At the present time, BCG has been given a lot of interest as cardiorespiratory signals. Apart from the above-mentioned thanks to the information technology revolution, including algorithms, machine learning approaches have been implemented hardware technology as well as software and services. BCG for measuring heartbeats. However, manual labeling of training sensors can be embedded in ambient environments without data is a restricting property. Furthermore, the training step the need for medical staff presence. Consequently, it has an should be repeated whenever the data collection protocol has arXiv:1807.00951v1 [eess.SP] 3 Jul 2018 been changed. outstanding impact in current e-health systems. Ultimately, BCG helps reduce checkups’ stress and the patient emotion Index Terms—Ballistocardiogram, Vita signs, Nonintrusive and attention responses. Fig. 1(a) shows an example of a monitoring, Technology and services for home care. typical BCG signal, while Fig. 2(b) shows an example of a typical electrocardiogram signal. The BCG waveforms may I.INTRODUCTION be grouped into three main groups, i.e., the pre-systolic ALLISTOCARDIOGRAPHY (BCG) is a noninvasive (frequently disregarded), the systolic and the diastolic as given technique for creating a graphical representation of the in Table I. The I and J waves are also quoted as ejection Bheartbeat-induced repeated motions of the human body. These waves [1]. To this extent, the definition, formal limitations, repeated motions happen due to the rapid acceleration of blood and nomenclature of ballistocardiography were discussed. The when it is ejected and moved in the great vessels of the body formal limitations were mainly due to the complexity of the during periods of relaxation and contraction, known as diastole used system and misinterpretation of the obtained signals and and systole, respectively. In other words, BCG can provide its deformations. The field of ballistocardiography has been revived as a result of the numerous technological advancements, I. Sadek is with Image and Pervasive Access Laboratory (IPAL), CNRS as, for example, the advent of microprocessors and laptop UMI 2955, Singapore e-mail: (see [email protected]). computers. All in all, ballistocardiography can be very useful REVIEW PAPER 2

(a) (b)

Fig. 1. (a) Example of a typical BCG signal with letters used to designate the waves. The arrow indicates the position of the beginning of the electrical ventricular systole (QRS. complex of the electrocardiogram). Image adapted from [2], [3], [1], (b) Aortic arch and force vectors coming from blood ejection by the left ventricle. Image adapted from [4].

QRS Complex TABLE I NOMENCLATUREOFBALLISTOCARDIOGRAM (NORMALDISPLACEMENT) R SIGNAL [7], [1].

Pre-Systolic Group (See Fig. 1(a)) F wave: (rarely seen) headward wave preceding G, related• to pre-systolic events, not an after-vibration. G wave: small footward wave which at times precedes the H wave. • Systolic Waves (See Fig. 1(a)) ST PR Segment H wave: headward deflection that begins close to the peak of the R wave, Segment T • P maximum peak synchronously or near the start of ejection. I wave: footward deflection that follows the H wave, occurs early in systole. • J wave: largest headward wave that immediately follows the I wave, occurs• late in systole. K wave: footward wave following J, occurs before the end of systole. • Q Diastolic Waves (See Fig. 1(a)) PR Interval L and N waves: two smaller headward deflections which usually follow K. S • M wave: footward deflection between L and N. • Smaller subsequent waves may be visible and are named in sequence. QT Interval •

II.PIEZOELECTRIC POLYVINYLIDENE FLUORIDE-BASED Fig. 2. Example of a typical electrocardiogram signal. SENSORS The piezoelectric effect is the ability of some materials to in several applications such as monitoring of cardiac function produce an electric charge in response to applied mechanical and performance in addition to monitoring of sleep and sleep- stress. The polyvinylidene fluoride (PVDF) is an exciting disordered breathing [8], [9]. One of the most prominent piezoelectric material and is usually developed as a very thin features of ballistocardiography is the accessibility and ready- and easily bent film. If a pressure force is applied to the film, availability, which allows the system to be deployed in users’ it creates a mechanical bending and a shifting of positive and homes without affecting the users’ privacy and daily activities. negative charge centers in the film, which then results in an In what follows, we explain more in detail the various tools external electrical field. The charge generated from PVDF is and algorithms exist in the literature to analyze and interpret equivalent to the applied pressure. Therefore, PVDF is one ballistocardiography, wherein we look at what types of sensors of the suitable candidates for detecting the small fluctuations that can be used for signal acquisition and what types of generated by different body parts [10]. software algorithms that can be used to extract vital information Wang et al.[11] proposed to use a PVDF piezopolymer such as heartbeat, respiration, and body movements. film sensor for unconstrained detection of respiration rhythm REVIEW PAPER 3 and pulse rate. The film sensor was placed under the bed- particular susceptibility to motion artifacts caused by subject sheet at the location of the thorax to obtain the variations of movements that might have led to the misidentification of the the pressure on the bed attributable to respiratory movement peak for autocorrelation functions. and heartbeats. The authors used the wavelet multiresolution Paalasmaa and Ranta [14] applied an unsupervised learning decomposition to compute the respiration and heartbeat. The approach on ballistocardiogram signals to compute heartbeat. output of the respiratory inductance plethysmography (RIP) The ballistocardiogram signals were collected from three and electrocardiography (ECG) were used as a reference for subjects using a piezoelectric pressure sensor over 5 hours respiration and heartbeat, respectively. The objective of the recording. To start with, feature vectors were extracted from wavelet analysis was to decompose the raw signal into low- the signal at possible heartbeat positions, i.e., the local maxima frequency components and high-frequency components. Next, of the signal. Then, a complete-link clustering was applied the component presenting a good agreement with either the to the feature vectors to look for a cluster with the highest respiratory movement or the heartbeat was selected. Afterward, density. The positions of the feature vectors of the densest the respiratory rate was computed directly based on a time- cluster were found to match real heartbeat positions in the varying adaptive threshold. On the other hand, the heartbeat signal. An angular dissimilarity measure was adopted since it component was first squared to rectify it into unipolar, and omits the differences in feature vector amplitudes. The sensor then the envelope of the rectified signal was calculated using a was located close to the patient’s upper body so that it can moving average smoothing algorithm. At last, a time-varying register cardiac activity in a proper way. adaptive threshold was also applied to the smoothed envelope Paalasmaa et al.[15] introduced a sleep tracking web appli- to compute the heart rate. It should be noted that heart rate cation, which was based on measurements from a piezoelectric detection was very challenging because the pressure variations film sensor placed under the mattress topper. The raw data attributable to heartbeat on the bed was very weak, and the coming from the sensor was sent to a web server for analysis shape of the signal was not always uniform. Another study was and extracting information. This information includes heart proposed by Wang et al.[12] to detect respiration rhythm and rate, respiration, sleep staging, and stress reactions. The heart pulse rate of premature infants using PVDF sensor array. The rate was computed by creating a heartbeat template using system was tested in clinical environments on five premature complete-link clustering [14], then the heart rate intervals were infants (1 male and 4 females). The main challenge of the detected by selecting those intervals that minimize a prede- proposed system was frequent body movement of the infants termined residual error. The sleep staging was carried out by and the weakness of the heartbeat vibration. utilizing heart rate variation, respiration variation, and activity Niizeki et al.[13] suggested using a PVDF sensor array information. The proposed approach was validated against a for unconstrained monitoring of respiration and heart rate. 40-patient group at a sleep clinic. The added value of this work The sensor array consisted of eight PVDF cable sensors and is the suitability of the system for long-term monitoring of they were horizontally integrated with a textile sheet on a bed sleep and the web application for sleep analysis at home. A surface covering the upper half of the body. The cardiorespi- more comprehensive study was introduced by Paalasmaa et ratory signals, i.e., BCG and respiration were obtained using al.[16] to compute heart rate from ballistocardiogram signals infinite impulse response digital filters. After extracting the acquired with piezoelectric film sensor. At first, a model for the cardiorespiratory signals, an optimal sensor selection search heartbeat shape was adaptively deduced from the signal using a routine was applied to select the most appropriate sensor. hierarchical clustering approach. Afterward, interbeat intervals The selection criterion was based on the magnitude of the were identified by detecting positions where the heartbeat shape power spectrum density (PSD). The autocorrelation functions best matches the signal. The proposed method was verified of the cardiac and respiratory signals were computed using with overnight recordings from 46 subjects in different settings, a 5-second and 15-second time segments for heartbeat and i.e., sleep clinic, home, single bed, and double bed. respiration, respectively. The outputs of the autocorrelation Chen et al.[17] advised to use four piezoelectric sensors functions were smoothed and differentiated using a Savitzky- to detect heart rate and respiration. One sensor was placed Golay (5 adjacent points) algorithm and finally, the heart rate under the pillow, whereas the other three were placed under and respiration were computed by measuring the intervals the mattress close to the back, hip, and calf level positions. The between the peaks for the respective autocorrelation functions. data was collected from five healthy subjects at age of twenties A fixed threshold was used to determine if the subject changes during a 2-hour’s nap in a sleep lab. ECG and nasal thermistor posture during the measurement, in which the output from signal were employed as heart rate and respiration references. the PVDF cables was disturbed to a large extent. A charge- Heart rate and respiration were computed based on the coupled device (CCD) camera was used to record the image multiresolution analysis of the wavelet decomposition in which of the body position during posture change as a time stamp. the Cohen–Daubechies–Feauveau biorthogonal wavelet was The proposed system was tested against thirteen healthy male selected as the basis function to design the decomposition and subjects whose ages ranged from 21 to 49 years. ECG and reconstruction filters. The 6th level approximation waveform pneumotachometer for measuring respiratory flow were used as was similar to the respiratory rhythm, while a combination a reference during the study. The study consisted of two phases, of the 4th and 5th scale coefficients were found to be suitable i.e., short-term recording for 10 minutes and an overnight for heart rate detection. The authors were able to measure study for 2 hours. For the overnight recording, only 7 subjects both vital signs from the four positions. However, the overall were involved. The proposed system had some limitations in optimal position was found in the back. That makes sense REVIEW PAPER 4 because the more the sensor is closer to the thorax, the more study, Brüser et al.[21] have implemented three different accurate the recovered signals are. methods using the same sensor set to measure the heart rate in A wheelchair-based system for monitoring the cardiac a nonintrusive way. Initially, the heart rate was computed using activity of its user was proposed by Pinheiro et al.[18]. The a sliding window cepstrum analysis [19]. Secondly, the heart signals were collected from piezoelectric film sensors and rate was computed using a Bayesian fusion approach, in which micro-electromechanical systems accelerometers installed in three estimators were calculated from each sensor channel such the seat and backrest of the chair. The system also included as adaptive-window autocorrelation, adaptive-window average photoplethysmography (PPG) sensors in the armrests. The magnitude difference function, and maximum amplitude pairs. data from the sensors were sent via Wi-Fi to a laptop with For each channel, these three estimator outputs were then a data acquisition board for deeper analysis. ECG recordings combined using a Bayesian fusion method to obtain an overall were used to validate the proposed system. The system was estimate. In other words, Bayesian fusion approach was applied tested in different situations, namely unmoving wheelchair, to 24 estimates. At last, the heart was estimated based on tiled floor motion, and treadmill tests. In the last two situations, the aforementioned approach. However, for each channel the ballistocardiogram signals collected from the piezoelectric separately. In general, the multichannel based approaches sensors were completely corrupted by motion artifacts. On improved the robustness of heartbeat interval estimation over a the other hand, the accelerometer was much more insensitive single sensor. More specifically, Bayesian-based method slightly to wheelchair motion. The analysis was done on seven outperformed the cepstrum-based method. subjects using the fast Fourier transform. Subsequently, the Martin-Yebra et al.[22] extracted heart rate variability prominent peak was selected within a specific frequency range indices from ballistocardiogram signals and then evaluated for heart rate estimation. In a summary, getting informative their correlation with electrocardiogram-derived ones. The ballistocardiogram signals from the piezoelectric sensors in a ballistocardiogram signals were acquired by a piezoelectric motion situation was almost impossible. However, it was more 3D-force plate in supine and standing positions, in a group convenient to get informative signals from the accelerometers of 18 healthy subjects (11 females). For each position, the and the PPG sensors. data collection was performed during 5 minutes. Furthermore, A multichannel approach was proposed by Kortelainen et subjects were asked to stay quiet to avoid any motion artifacts. al.[19] to extract heart rate and respiration information using The ballistocardiogram waves, i.e., (H, I, J, K) were detected eight PVDF sensor channels located in the upper position of by synchronizing ballistocardiogram signals with ECG signals. the bed. The heart rate was estimated by averaging the signal Although the proposed approach provided a good match with channels in the frequency domain, in which a sliding time the reference ECG, it is very difficult to generalize this approach window was utilized to compute the cepstrum of each signal for real-life deployment as the data collection was conducted channel. However, the respiratory rate was computed from the for a very short time and the detection part was achieved by first principal component of a principal component analysis adapting information from the ECG signals. (PCA) model applied to the low-pass filtered bed sensor signal. Katz et al.[23] measured cardiac interbeat intervals using a The assumption was that the first principal component will give contact-free piezoelectric sensor placed beneath the mattress the signal with the maximum variance, and as a result shall under the tested subjects. The data was collected from 25 improve the sensitivity for the extraction of the respiration. home sleep recordings of 14 healthy subjects in a two-in- Twenty-eight patients were recruited for the study and they were bed setting. The authors applied three algorithms to the suspected to have diverse kinds of sleep problems. Frequency collected ballistocardiogram signals as follows. First, interbeat domain averaging was better than simple averaging over all intervals were found by decomposing the signal into multiple the sensor channels. The extracted information, i.e., heart rate, components using an empirical mode decomposition filter and respiration, and movement might have been used for further then locating the candidate peaks within a localized search sleep analysis. area. Second, after locating potential interbeat intervals, a The same pressure bed sensor assembly with eight PVDF binomial logistic regression model was applied to classify sensors was applied for sleep apnea detection in [20]. The each interbeat interval into one out of three groups based respiratory signal was computed by two methods. The first on morphological properties of the ballistocardiogram signal. method was to apply a Hilbert transform to the bed sensor signal Finally, an additional algorithm was implemented to get discrete and then smooth the signal with a low pass filter. The second interbeat interval distribution maps during the night recording, method was similar to Kortelainen et al.[19] by adopting the considering interbeat interval data from overlapping 15 minutes PCA approach. At last, the amplitude baseline of the respiratory windows. The preceding three algorithms demonstrated the signal was estimated as the mean value of the preceding 100 effectiveness of the proposed system for heart rate variability seconds. An apnea event was detected if the ratio with the analysis. Sela et al.[24] used the same piezoelectric sensor to baseline was less than a selected percentage threshold value detect left ventricular ejection for 10 subjects (6 males and 4 for a period of at least 10 seconds. The authors applied their females), where the lower body of each subject was enclosed methodology to twenty-five patients out of twenty-eight patients in a negative pressure chamber. The negative pressure chamber recruited in [19]. The system showed a good agreement with regulates and controls the blood pressure of the participants. the reference polysomnography. However, the authors used This study demonstrated the ability of the system to identify the simplified reduced respiratory amplitude index (RRAI) internal bleeding condition among patients at risk, namely instead of the standard apnea-hypopnea index (AHI). In another individuals after an accident or surgical operation. REVIEW PAPER 5

Alvarado-Serrano et al.[25] measured beat-to-beat heart rate subjects (1 male and 1 female) for 5 minutes. After visual from subjects sitting in a common office chair. The authors used inspection versus the reference ECG, it was found that the a piezoelectric sensor fixed to the bottom side of the seat to acquired waveforms closely simulate those reported in the collect ballistocardiogram signals from seven subjects (5 males literature. Equivalent results were also reported by Junnila et and 2 females). Continuous wavelet transform with splines was al.[30], [31], which presented the suitability of the EMFi implemented to detect beat-to-beat intervals in which an optimal sensors for extracting ballistocardiogram signals. scale was selected to reduce noise and mechanical interferences. A smart mattress was developed by Koivistoinen et al Thenceforth, learning and decision phases where applied to the [32] to detect interbeat intervals in a nonintrusive way from selected scale to detect potential J-peaks. In the learning phase, six male subjects. The mattress consisted of 160 EMFi the first four heartbeats in the ballistocardiogram signal were electrodes distributed throughout the mattress that enabled found to define initial thresholds, search windows, and interval signal acquisitions from multiple locations. Two methods limits. The learned parameters were then utilized to determine were implemented to detect interbeat intervals, i.e., a pulse the next heartbeat and were readopted after each heartbeat method and an adaptive window cepstrum method. In the detected to adhere to the heart rate and signal-amplitude former, signals from all channel were high pass filtered and changes. A similar study was proposed by Liu et al. [26]. then squared. After that, these squared values were averaged However, two PVDF film sensors were installed in the seat between all channels and low-pass filtered the result. At last, the cushion and foot insole. beginning of each heart rate was tracked in the generate pulse Choe and Cho [27] used a piezoelectric sensor installed train signal. In the latter, the window length of the cepstrum between a bed-frame and a mattress for unconstrained mon- was selected using the pulse method as the first estimator of itoring of heart rate. The data was collected from 7 male the heart beats. Then, signals from all channels were averaged subjects sleeping in a supine sleeping position where the in the frequency domain. An interpolation was used to detect sensor was placed under the subject’s back. In total, they more accurate location for the selected cepstrum maximum collected ballistocardiogram signals for about 5 hours from value. Moreover, the motion artifacts were eliminated based on all subjects, in which subjects were not moving during data the signal variance using a sliding time window. Although the acquisition. The data was first smoothed using a moving mean cepstrum-based method provided better results than the pulse absolute deviation, then the J-peaks were detected within a method, its computational efficiency was not as good as the specific search region using an adaptive thresholding technique. adaptive window method. The authors achieved satisfactory results with the reference Aubert et al.[33] adopted a single EMFi sensor to provide ECG. However, this method may not be applicable in real-life heart rate, breathing, and an activity index representing body applications because the data was not collected in a typical movements. The recommended system was validated utilizing sleep sitting and the motion artifacts were not considered as data collected from 160 subjects (58 males and 102 females) well. Table II summarizes the unconstrained monitoring of for a total of 740 hours. Part of the data was collected in a vital signs using the PVDF-based sensors. sleep laboratory from patients (i.e., sleep apnea, insomnia, and other sleep disorders) who underwent a full polysomnography III.ELECTROMECHANICAL FILM-BASED SENSORS and the other part was collected at home from healthy subjects. The electromechanical film (EMFi) material is a plastic Body movements were first isolated from the sensor data based film that can transform mechanical energy into an electrical on the signal amplitudes and energy, and their time derivatives. signal and the other way around. Basically, it is a flexible Thereafter, heart rate was measured using a sliding window and thin bi-axially oriented polypropylene film covered with autocorrelation method, in which the optimal window length electrically conductive layers, which are enduringly polarized. had to span 3 to 5 consecutive beats. The respiratory rate was EMFi has a static charge reaching hundreds of Volts. When estimated based on the local peaks, troughs, and zero-crossings, a pressure is applied to the film, a charge is created on its constrained to rules ensuring physiological validity in terms of electrically conductive surfaces and this charge can be measured duration and amplitude. Across the 60 subjects, the vital signs as a current or voltage signal, usually with a charge amplifier. were computed over epochs of 30 seconds and the average As a result, the EMFi serves as a sensitive motion sensor values were computed and compared to the reference ECG and [28]. Alametsä et al.[28] suggested to use EMFi sensors thorax belt, respectively. The recommended system achieved for obtaining ballistocardiogram signals from certain places satisfying results compared to the reference devices. of the body. The authors installed EMFi sensors in a chair Kärki and Lekkala [34] used EMFi and PVDF sensors in the and in smaller pieces in a few positions on the body (arm, measurements of heart rate and respiration. The objective of leg, and chest). The ballistocardiogram signals were collected the study was to determine if there were differences between from a few people and the duration of the recordings was the results of both sensors. ECG was used as a reference for relatively short. This study demonstrated the potential of the heart rate and a thermistor for respiration rate. Heart rate and EMFi material in monitoring the changes in cardiac function. respiration were measured using power spectral density (PSD). In another study, Koivistoinen et al.[29] evaluated the ability The two sensors were embedded inside a textile pocket and the of the EMFi sensors for measuring ballistocardiogram signals. pocket itself was integrated into clothing. They were positioned The authors installed two EMFi sensors in the seat and backrest underneath a commercial heart rate belt on the left side of the of a normal chair, and the data was collected from two young sternum. Preliminary results showed that both sensors provided reliable results in the measurements of heart and respiration REVIEW PAPER 6 : : HR , TM . , FEMALE : F , LABORATORY : MALE : Lab M , , LINKAGECLUSTERING - COMPLETE : WINDOW AVERAGE MAGNITUDE DIFFERENCE - CLC PREMATURE INFANTS , : Outcome HR, RR HR, RR HR, RR HR HR, RR HR HR, RR HR HR, RR Apneas HR HRV HR LVET HR HR HR ADAPTIVE : SECONDS P. Infants : , CONTINUOUS WAVELET TRANSFORM Sec AMDF , : , CWT , HOURS : Duration N/A 10 Min 10 Min, 2 Hrs 330 Min Overnight Overnight 2 Hrs 5 Min Overnight Overnight Overnight 5 Min Overnight 84 Min 100 Sec 45 Min 67 Min Hrs NOT AVAILABLE , : N/A , THRESHOLD : MINUTES TH : , Min , MAXIMUM AMPLITUDE PAIRS : Deployment Lab Hospital Home Lab Sleep clinic Sleep clinic, home Lab Wheelchair Hospital Hospital Hospital Lab Home Lab Chair Lab Lab MAP , WAVELET TRANSFORM : TABLE II WT . EMPIRICALMODEDECOMPOSITION AUTOCORRELATIONFUNCTION : : Subjects (M, F) N/A 5 P. Infants (2 M and 313 F) M 3 N/A 40 N/A 60 N/A 5 N/A 21 N/A 6 N/A, 15 M,15 13 M, F 13 F 15 M, 13 F 17 M, 11 F 14 N/A 6 M, 4 F 5M, 2 F 7 M 6 N/A BASEDSENSORS ACF EMD , , PVDF- PRINCIPAL COMPONENT ANALYSIS : PCA , RESPIRATORY RATE : Method WT WT ACF CLC CLC, TM CLC, TM WT FREQ CEP, PCA PCA ACF, MAP, AMDF ECG Sync EMD N/A CWT Adaptive TH CWT CEPSTRUM RR : , CEP , [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] FREQUENCY ELECTROCARDIOGRAMSYNCHRONIZATION : : FREQ HEART RATE VARIABILITY , : ECG Sync , HRV , FUNCTION HEART RATE UMMARYOFUNCONSTRAINEDMONITORINGOFVITALSIGNSUSING TEMPLATE MATCHING S REVIEW PAPER 7 rates. However, the PSD was not robust enough because the In the same way, Zink et al.[39] used an EMFi sensor peak in the spectrum might get wider and multiple peaks to detect heartbeat cycle length in patients suffered from might have appeared. Another study was proposed by Kärki atrial fibrillation and sinus rhythm. The sensor was placed and Lekkala [35] to determine heart rate with EMFi and PVDF under the bed-sheet and data was collected from 22 patients materials. The EMFi and PVDF sensors were grouped together (15 M, 7 F) during and after cardioversion. Cardioversion to a form a single structure. The data was collected from 10 is a medical procedure that returns a normal heart rhythm subjects (5 males and 5 females) over 60 seconds recording in people with certain types of abnormal heartbeats, namely (sitting and supine positions), where the sensor structure was arrhythmias. In another study, Zink et al.[40] employed the placed under the legs of a chair and bed. These preliminary EMFi sensor to measure heartbeat in patients suffered from results demonstrated that the heart rate can be measured at sleep-disordered breathing. Twenty-one patients (19 males, 2 home just by sitting on a chair or lying in a bed. females) were recruited for the study and underwent a standard Pinheiro et al.[36] introduced a low-cost system to measure full-night polysomnography. A quality-index was proposed blood pressure variability and heart rate variability. A single based on the three estimators previously discussed in [38] that EMFi sensor was installed in the seat of a normal office allowed to identify segments with artifacts and to automatically chair to measure ballistocardiogram signals while a finger PPG exclude them from the analysis. The proposed system provided was used to estimate arterial oxygen saturation (SpO2). For good correlation of beat-to-beat cycle length detection with validation, ECG was acquired using three chest leads. Using simultaneously recorded ECG. LabVIEW, heart rate and heart rate variability were determined Pino et al.[41] used two EMFi sensors installed in the seat by an adaptive peak detection algorithm. The pulse arrival time and backrest of a normal chair in order to measure heart rate. was estimated as the time difference between ECG and PPG Ballistocardiogram data were collected from 54 individuals, maximum peaks, and when considering BCG-PPG relation, whereas 19 of them were measured in a laboratory (1 minute) the I-valley (Fig. 1(a)) was the reference. The designed system and the rest in a hospital waiting room (2 minutes). Firstly, was appraised using data collected from five healthy volunteers empirical mode decomposition and wavelet analysis were over 10 minutes recording. The preliminary study demonstrated (Deabuchie 6) implemented to reconstruct ballistocardiogram that heart rate variability can be measured using the correlation signal. Secondly, the J-peaks of the ballistocardiogram signal between BCG and PPG. The PSD was exploited to measure was detected using a length transform analysis. The body the heart rate. In another study, Pinheiro et al.[37] collected movements were eliminated using a moving time window. ballistocardiogram signals by placing an EMFi sensor in the Then, for each time-window two thresholds were computed, backrest of a wheelchair’s, beneath the lining. Two modulation- i.e., T1 = (max + min)/2 and T2 = mean + 1.1 std, if T1 ∗ based schemes were carried out for heart estimation, i.e., a was greater than T2, the current window was marked as a body sliding power window and an all-peak detector. The objective movement. The wavelet analysis was preferred to reconstruct was to find all local maxima and local minima, then a spline the signal as it produced a higher effective measurement time. interpolation and a moving power window were employed to A similar approach was also proposed by Pino et al.[42]. compute a modulating signal. At last, a fast Fourier transform However, they increased the size of the dataset to 114 people. was applied to the output of each method in order to measure Of those, 21 were gathered in a school (2 minutes), 42 in the average heart rate from the signal’s fundamental frequency. homes (2 minutes), and 51 in a hospital waiting area. It is This system was evaluated using data gathered from six normal difficult to assess the robustness of this system because the subjects (4 males and 2 females) during 125 seconds. data was collected in a very short time and in a controlled Brüser et al. [38] proposed an unsupervised approach to environment as well. determine inter-beat intervals using an EMFi sensor. The sensor In a recent study, Alametsä and Viik [43] presented the was fixed underside of a thin foam overlay which was thus stability of ballistocardiogram signal during 12 years’ time, located on top of the mattress of a typical bed. The system on which the data was gathered from a single person in a was evaluated on over-night recordings from 33 individuals (14 sitting position using EMFi sensors. Several other signals were males and 19 females). Three estimators were implemented, recorded as well such as ECG, ankle pulse signal, and the namely autocorrelation function, average magnitude difference carotid pulse signal from the neck near the carotid artery. All function, Maximum amplitude pairs in order to compute the measurements lasted about 2 to 3 minutes with a sampling local interval length using a sliding time window. Ideally, this frequency of 500 Hz. In a conclusion, ballistocardiogram window contained two events of interest. The values of the research may be recommended for examining long-term local interval length were constrained by two thresholds, i.e., changes in heart operation and to reveal variations in it. Table III Tmin and Tmax. The body movements were detected based summarizes the unconstrained monitoring of vital signs using on the maximum amplitude range of each time-window. The the EMFi-based sensors. information from the three estimators was then applied to a probabilistic Bayesian method to estimate the inter-beat IV. PNEUMATIC-BASED SENSORS intervals in a continuous manner. Although the proposed The idea of the pneumatic system is to deploy a thin air- method achieved very satisfactory results, the main limitation sealed cushion between the bed and mattress. Thereafter, when existed in the implicit hypothesis that two successive heart a person rests in the bed, the forces originated because of the beats in the BCG have an unknown but similar morphology. heartbeat, respiration, snoring and body movements affects This assumption may not always hold true. REVIEW PAPER 8 RESPIRATORY : RR , HEART RATE : HR , . WINDOW AVERAGE MAGNITUDE DIFFERENCE - FEMALE : F , ADAPTIVE MALE Outcome BCG HR HR, RR HR, RR HR, RR HR, BP HR HR HRV HR HR HR : : LABORATORY : M , Lab AMDF , , NOT AVAILABLE Duration 5 Min Overnight Overnight 60 Sec 30 Sec 10 Min 125 Sec Overnight N/A Overnight 1 Min, 2 Min 2 Min, 2 Min : N/A , LINEARTRANSFORM : LT , MAXIMUM AMPLITUDE PAIRS : MAP Deployment Lab Lab sleep Lab, Home Lab Lab Lab Lab Clinic Hospital Hospital Lab, Hospital Home, Hospital , THRESHOLD : WAVELET TRANSFORM TH : , TABLE III WT . CEPSTRUM : CEP , Subjects (M, F) 1 M, 1 F 6 M 58 M, 102 F N/A 5 M, 5 F 5 N/A 4 M, 2 F 14 M, 19 F 15 M, 7 F 19 M, 2 F 54 N/A 114 N/A SECONDS BASEDSENSORS - : I Sec , EMF HOURS : EMPIRICALMODEDECOMPOSITION : Hrs , EMD Method Visually CEP Adaptive TH, ACF PSD PSD PSD PSD ACF, MAP, AMDF ACF, MAP, AMDF ACF, MAP, AMDF EMD, WA, LT EMD, WA, LT , MINUTES : [32] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] Min FUNCTION , AUTOCORRELATION FUNCTION : ACF , UMMARYOFUNCONSTRAINEDMONITORINGOFVITALSIGNSUSING S RATE REVIEW PAPER 9 the air in the cushion through the mattress. This slight human signs using the pneumatic-based sensors. movement causes a pressure and therefore variations in pressure are measured by a supersensitive pressure sensor [44], [45]. V. STRAIN GAUGES-BASED SENSORS Watanabe et al.[46] used the aforementioned pneumatic Brink et al.[51] implemented four force sensors under bed- system to measure heartbeat, respiration, snoring, and body frames to unobtrusively record heartbeat, respiration activity, movements in a noninvasive manner. The three bio-signals, and body movements. Each force sensor consisted of a reflex namely heartbeat, respiration, and snoring were detected using light barrier sandwiched between two aluminum plates. When a band-pass filter with different cutoff frequencies. Following, a force is applied to the sensor, the two aluminum plates windowed Fast Fourier transform algorithm was applied to are squeezed together slightly and the distance between them measure heart rate and respiration. However, the relative decreases. The reflex light barrier senses the distance between magnitude of snoring was calculated by the standard deviation the two plates and converts it into a voltage signal, which of the filtered snoring signal and the relative magnitude of is analogous to the ballistic forces of the heart. This voltage body movements was calculated as the standard deviation of signal is then pre-amplified and passed through a low-pass the envelope of the sensor output signal. The authors validated filter to eliminate ripple and noise. In this preliminary study, the proposed system using data collected from 15 subjects heartbeat and respiration were detected by finding local minima (12 males and 3 females) over 15 nights. Preliminary results or maxima in the signal within a sliding window. To evaluate showed good agreement against reference devices, namely ECG, the robustness of the force sensors, the signals were acquired belt-type respirometer, and a snoring detection microphone. from four subjects (2 males and 2 females) and in different The body movements were identified and recorded by a CCD conditions, i.e., three types of single beds, three types of frames, camera. In another study, Kurihara and Watanabe [47] acquired two types of mattresses. In total, seventy-two conditions were data from 10 subjects (20 seconds each) to measure heart rate evaluated. In each condition, subjects were asked to sleep and respiration. In this study, a condenser microphone was used in a relaxed supine position on the bed. The signals were as a reference for heart rate, respiration and signal-to-noise ratio. collected during 5-minute recording from the four force sensors. Validation results demonstrated that the pneumatic system was Additionally, ECG signals were also collected as a reference. more susceptible to environmental noise, for example, opening Preliminary results showed that the proposed system can be and closing the door than the reference condenser microphone. an acceptable tool for computerized and unattended sleep-data Chee et al.[48], [49] recommended to use a balancing collection over a lengthy period. tube between two air cells to improve the effectiveness of Inan et al.[52] collected ballistocardiogram signals using posture changes during data collection. Balancing tube with strain gauges within a modified commercial scale. The signals a high air resistance aimed at equalizing the pressure of each were collected from twenty-one subjects (11 males and 10 air cell within a certain time constant. More precisely, it females), on which participants were asked to stand as quiet performed the role of a high-pass filter to eliminate body as possible on the scale for 45 seconds while BCG and motion. The air-mattress system consisted of 19 air cells, in ECG were concurrently recorded. In this study, the measured which measurements can be performed between any pair of ballistocardiogram signals from all subjects closely resemble cells. However, the authors collected data from the two cells those reported in the literature. Besides, the system was able situated on the backside of the chest and abdominal region. to provide beat-to-beat cardiac output monitoring. Additionally, Signal was collected from a single subject laying on the air ballistocardiogram measurements were found to be repeatable mattress where ECG and nasal airflow signal were collected over 50 recordings collected from the same subject over a three- simultaneously. Although the balancing tube helped eliminate week period. The proposed solution was more susceptible to body motion, it affected the sensitivity of the measurement. motion artifacts because the signals were acquired in a standing Heart rate was measured by finding the maximum peak of position. Hence, it might not be suitable for older adults who the BCG signal between the two R-R peaks of the ECG cannot stand as tranquil on the scale. In order to eliminate signal. On the other hand, the respiratory rate was measured floor vibrations, Inan et al.[53] proposed a seismic sensor, by windowed fast Fourier transform, i.e., short-time Fourier i.e., geophone, located in proximity to the modified scale that transform (STFT). Preliminary results showed good match served as the noise reference. An adaptive algorithm was then against reference devices. Nevertheless, the proposed system implemented to filter the output of this sensor and cancel might not be a preferred choice for large-scale deployment due the vibrations from the measured ballistocardiogram signal. to its complexity. In another study, Shin et al.[50] applied the Signals were collected from a healthy volunteer while another same air mattress for uncontaminated measurement of heart rate person stomped around the scale, hence producing increased and respiration. In which, a total of 13 healthy male subjects floor vibrations. Furthermore, signals were also collected from were involved in the validation study, i.e., four hours study. another volunteer standing inside a parked bus while the The authors measured the heart rate from the R-peaks of the engine was functioning. This research established that ballisto- ECG, while the respiratory rate was measured manually. In cardiogram recording is feasible in almost all environments, addition, the authors asked three subjects to simulate sleep including ambulances and other transport vehicles, as long as apnea (breath-holding) five times each for 10 to 15 seconds. the vibrations are not so significant to rail the electronics or Thereafter, the apneas were detected based on the variance lead to a distorted version of the ballistocardiogram force to of the respiratory signal with a moving window technique. be coupled to the scale. Table IV summarizes the unconstrained monitoring of vital REVIEW PAPER 10 : Hrs , MINUTES : Min , RESPIRATORY RATE : RR , HEART RATE : . Outcome HR, RR, SI HR, RR HR, RR HR, RR HR , FEMALE : LABORATORY F : , Duration Overnight 20 Sec N/A 4 Hrs Lab , MALE : M , Deployment Lab Lab Lab Lab OURIERTRANSFORM NOT AVAILABLE F : N/A . TIME TABLE IV - SHORT : Subjects (M, F) 12 M, 3 F 10 N/A 1 N/A 13 M STFT , BASEDSENSORS - SECONDS : NEUMATIC P Sec , Method STFT STFT ECG Sync, STFT ECG Sync, STFT HOURS [46] [47] [48], [49] [50] UMMARYOFUNCONSTRAINEDMONITORINGOFVITALSIGNSUSING S REVIEW PAPER 11

In the same way, Inan et al.[54] evaluated the elec- from nine subjects (5 males, 4 females) during 1-hour recording. tromyogram signal collected from the feet of the subject The beat-to-beat intervals provided by this approach can be during ballistocardiogram recording as a noise reference for adopted in determining frequency domain heart rate variability standing ballistocardiogram measurements. As the lower-body that is most frequently used in the assessment of sleep quality. electromyogram signal can be collected directly from the Lee [59] et al. proposed to use load cells, installed under footpad of the modified scale, the proposed system is self- bed supports, to measure heart rate and respiration for infants. contained and can automatically eliminate motion artifacts. In Four infants (5 to 42 months) were involved in the study and a another study, Wiard et al. [55] used a motion sensor instead of total of 13 experiments were carried out between 10 to 178.8 electromyogram sensors to record body motions and to serve as minutes. Initially, heart rate and respiratory components were a noise reference. The added value of the motion sensor was to extracted using band-pass filters of various cutoff frequencies. provide a minimum delay between the motion-related noise in For the heart rate component, a first-order differentiation the measured signal and the noise detected by the motion sensor. filter was applied, thus a nonlinear transformation, i.e., a This minimum delay provided the time resolution needed to Shannon entropy was applied to the differentiated signal to flag single heartbeat events, hence maximizing the refinement obtain only positive peaks. Additionally, a moving average of the approach. filter was employed to flatten out the spikes and noise bursts. Brüser et al.[56] introduced an unsupervised learning At last, heart rate was measured by finding local peaks in approach to measuring heartbeat in a noninvasive manner. an optimum signal. For the respiration component, as the Ballistocardiogram signals were recorded by strain gauges band-pass filtered signal contained residual baseline drift, a in a Wheatstone bridge configuration attached to the slat detrending algorithm based on empirical mode decomposition under the mattress of a hospital bed. A high-pass filter was was adopted to get rid of such unwanted trend. Similar to heart applied to the raw data in order to remove low-frequency rate, local peaks were detected in the detrended signal and respiratory components. Next, a set of features, representing the therefore the respiratory rate was measured. A signal quality fundamental morphology of the heartbeat, were extracted from index was developed to choose the optimum signal out of the a 30-second time segment. Afterward, the principal component four load cells’ signals. The quality processing procedure was analysis was applied in order to reduce the dimensionality of the developed based on calculating a threshold value computed feature vectors. Additionally, a k-means clustering algorithm from an autocorrelation function and a power spectral density was adopted to identify clusters of feature vectors. This training function. The proposed system achieved acceptable results step resulted in a list of estimated heartbeat locations. The compared to the reference ECG and respiratory belt. Table V parameters obtained during the training step were thus manip- summarizes the unconstrained monitoring of vital signs using ulated to locate heartbeats in the remaining ballistocardiogram the strain gauges-based sensors. signal by merging the results of three independent indicator functions, i.e., cross-correlation, Euclidean distance, and heart VI.HYDRAULIC-BASED SENSORS valve signal. Finally, the estimated heartbeat locations were The concept of the hydraulic sensor is to measure the change exploited to provide an improved list of beat-to-beat periods. in pressure applied to a liquid-filled tube. For example, Heise Signals were captured from sixteen healthy subjects (9 males et al.[60] designed a hydraulic based-sensor for unrestrained and 7 females) during thirty minutes switching their positions monitoring of heart rate and respiration. Preliminary data every 7.5 minutes (left lateral, supine, right lateral, prone). were collected from two individuals (1 male and 1 female). This method produced good agreement with the reference Participants were instructed to lie on a bed for approximately ECG. However, the primary limitation was the training step as 10 minutes. During the 10 minutes, they were asked to lie it had to be repeated whenever subjects enter the bed or adjust on the back, on the right side, on the back again, on the left their posture with regard to the ballistocardiogram sensor. side, and on the back once more (2 minutes each position). Nukaya et al.[57] provided a contact-free method for unob- In this preliminary research, heartbeat signal was extracted trusive measuring of heartbeat, respiration, body movement, and by detecting the difference between the most negative and position change. The authors collected the pressure data using the most positive points within a moving window. After that, four piezoceramics transducers set beneath bed supports. The a low-pass filtered was applied to reduce the effect of noise proposed system was able to detect previous bio-signals without and smooth the signal. A fixed threshold was employed to the need for a preamplifier, accordingly without any voltage detect a body motion. Finally, the heart rate was measured by source. This is because the sensing devices were distortion adopting the autocorrelation function. However, the respiratory sensors that operate without an electrical power supply, i.e., rate was measured by low-pass filtering the signal and then they produce voltage according to the time derivative of the subtracting the DC bias. Afterward, the zero-crossings were distortion. counted to provide the breaths per minute. Preliminary results Vehkaoja et al.[58] introduced dynamic pressure sensors approved that the hydroponic sensor was effective at extracting for detecting heartbeat intervals of an individual sleeping heart rate and respiration against the reference devices, namely on a bed. The pressure sensors were composed of EMFi a piezoresistive device worn on the subject’s finger and material and located under the bed supports. In this study, respiration band wrapped around the subject’s torso. In a individual heartbeats were not observed. However, the intervals different study, Heise et al.[61] have validated the sensor in which the correlation between two successive signals using data collected from five subjects (3 males and 2 females) segment maximized. Ballistocardiogram signals were collected REVIEW PAPER 12 : Hrs , . MINUTES : CORRELATION - LABORATORY Min : , Lab CROSS , : CCF , RESPIRATORY RATE : RR , Outcome HR, RR HR HR HR HR, RR HEART RATE : EMPIRICALMODEDECOMPOSITION HR : , PRINCIPAL COMPONENT ANALYSIS EMD : , FEMALE PCA : , F , Duration 5 Min 45 Sec 30 Min 1 Hrs 10 - 178.8 Min MALE : M , HANNONENTROPY :S SE , Deployment Lab Lab Lab Lab Home NOT AVAILABLE : N/A TABLE V . ELECTROCARDIOGRAMSYNCHRONIZATION : Subjects (M, F) 2 M, 2 F 11 M, 10 F 9 M, 7 F 5 M, 4 F Infants (3 M, 1 F) BASEDSENSORS - AUTOCORRELATIONFUNCTION : ECG Sync , ACF , NEUMATIC P MAXIMUM / Method SWM/M ECG Sync PCA, K-means CCF, ED, HVS ACF SE, EMD, SWM/M HEART VALVE SIGNAL : [51] [52] [56] [58] [59] HVS , SLIDINGWINDOWMINIMUM : SWM/M , UCLIDEAN DISTANCE :E ED SECONDS , : Sec , FUNCTION HOURS UMMARYOFUNCONSTRAINEDMONITORINGOFVITALSIGNSUSING S REVIEW PAPER 13 and have confirmed stability of the signal processing algorithms subjects (2 males and 1 female) during 10 minutes recording using real and synthesized signals. and four older adults (4 males) in a typical home environment. Rosales et al.[62] deployed four hydraulic transducers under This approach provided slightly better results compared to the the bed mattress, covering the upper part of the body in order clustering-based approach provided by Rosales et al. [62]. to measure heart rate in a nonrestrictive way. Each transducer In order to address the uncertainty inherent in a ballistocar- was connected to a pressure sensor to record the pressure diogram signal, for instance, misalignment between training forces applied to it. In this preliminary study, heartbeats were data and ground truth, improper collection of the heartbeat by computed using a clustering-based approach as follows. Every some transducers, Jiao et al.[66] applied the Extended Function five seconds, body motions were eliminated based on the of Multiple Instances (eFUMI) algorithm to ballistocardiogram variance of the transducers’ signal. Following body motions signals generated by the four hydraulic transducers. The removal, the transducer’s signal was band-pass filtered to objective of the eFUMI was to learn a personalized concept remove respiratory components and filtered once more using of heartbeat for a subject in addition to several non-heartbeat an average filter to smooth the signal prior to feature extraction. background concepts. Following the learning step, heartbeat Afterward, three features were extracted from every 5-second detection and heart rate estimation can be applied to test data. time window based on the IJK points of the ballistocardiogram The limitation of this algorithm is the need for sufficient training signal. In addition, the extracted features were classified into data, which might not be always available. two groups using k-means clustering algorithm. The first group, Rosales et al.[67] applied the clustering-based approach [62] i.e., the smallest cluster was assigned to the heartbeat class. and the Hilbert transform approach [63] to ballistocardiogram Then, the second group, i.e., the largest cluster was assigned signal collected from four male senior residents. The signals to the non-heartbeat group. In conclusion, the heartbeats’ (J- were collected from residents over a two to four months period peaks) locations were compared to a reference signal obtained under in-home living conditions. However, the analysis was from a piezoresistive device worn on the subject’s finger. Data done only over five minutes of initial recordings. The Hilbert were acquired from four subjects (2 males and 2 females) transform approach was able to produce more stable heart rate during 6 minutes (supine position). Although such clustering- estimates compared to the clustering-based approach. The latter based approach might have provided good results it might approach was more susceptible to motion artifacts. Table VI only be applicable to specific situations. Furthermore, to think summarizes the unconstrained monitoring of vital signs using the presented method to be applied in practical applications, the hydraulic-based sensors. manually labeling (training) data is, however, a restricting property. VII.FIBER OPTIC-BASED SENSORS A similar study was proposed by Su et al.[63]. Nonetheless, In existing literature, unobtrusive vital signs monitoring the heart rate was measured using the Hilbert transform and is achieved either by microbend fiber-optic sensors (MFOS) the fast Fourier transform (30-second window). In this study, or fiber Bragg grating sensors (FBGS). The principle of the ballistocardiogram signals were acquired from five subjects (3 MFOS is that if an optical fiber is bent, insignificant amounts males and 2 females) during 2.5 minutes in a supine position. of light are lost through the fiber walls. This reduces the This approach provided a lower error rate compared with the amount of received light and is a function of bend pressure windowed peak to peak deviation (WPPD) method introduced [68], [69], [70], [71]. The FBG is an optical fiber that serves by Heise et al.[60]. Although results were consistent with as a filter for a specific wavelength of light. The principle of the reference device, ballistocardiogram signals were assumed the FBGS is to detect the reflected Bragg wavelength shift relatively stationary. This assumption is not always true because owing to changes in temperature, strain, or pressure [72], [73]. typically heartbeats are not uniform in time [64]. Fiber Bragg gratings are commonly used optical fiber sensors In another study, Lydon et al.[65] proposed a new algorithm for measuring temperature and/or mechanical strain. Though, to detect heart rate using the four hydraulic transducers. As the excessive cost of the interrogation systems is the most a first step, a band-pass filter was implemented to remove significant obstacle for their large commercial application [74]. the respiration component as well as high-frequency noise. Chen et al.[75], [76] described the effectiveness of the Next, the data from the four transducers were separated into MFOS for nonintrusive monitoring of heart rate and breathing 0.3-second (30 samples) segments and the short-time energy rate. For heart rate, ballistocardiogram signals were gathered profiles were computed for each segment. As a result, four hear from several subjects in sitting position and breathing normally. rate values were generated for each transducer by locating the Preliminary results have proved that the ballistocardiogram local peaks. Moreover, a single heart rate value was selected waveforms closely simulated those reported in the existing based on the DC level of each transducer’s signal. Typically, literature. For breathing rate, nine volunteers were involved in a higher DC level in the obtained transducer’s signal means the study in which respiratory signals were collected during that the transducer makes better contact with the body and sleep. The system has shown a good match with the reference therefore gives a more stable ballistocardiogram signal. Hence, respiratory device. Deepu et al.[77] introduced a smart cushion the transducer with the highest DC level was chosen for integrated with MFOS for real-time heart rate monitoring. The heart rate measurement. Finally, outliers were eliminated by cushion can be placed on the seat or back of a chair for data following whether the estimated heart rate value was more collection. In this study, five subjects were involved, and signals than 15 beats per minute from the moving average heart rate were collected during 5-minutes. Several steps were applied value. Validation data were collected from two groups, i.e, three REVIEW PAPER 14 : Hrs , MINUTES : Min , EXTENDEDFUNCTIONOFMULTIPLE : RESPIRATORY RATE : RR , eFUMI , HEART RATE : TIMEENERGY HR - , SHORT Outcome HR, RR HR, RR HR HR HR HR HR HR : FEMALE : STE F , , MALE : M Duration 10 Min 10 Min 6 Min 2.5 Min 10 Min Overnight 10 Min Overnight , . ILBERT TRANSFORM :H LABORATORY NOT AVAILABLE HT : Deployment Lab Lab Lab Lab Lab Home Lab Home : : Lab N/A , . TABLE VI INSTANCES BASEDSENSORS - Subjects (M, F) 1 M, 1 F 3 M, 2 F 2 M, 2 F 3 M, 2 F 2 M, 1F 4M 4 N/A 4 M CLUSTERINGAPPROACH : CA , YDRAULIC H Method WPPD WPPD CA HT STE eFUMI CA, HT [60] [61] [62] [63] [65] [66] [67] WINDOWEDPEAKTOPEAKDEVIATION : WPPD , SECONDS : Sec , HOURS UMMARYOFUNCONSTRAINEDMONITORINGOFVITALSIGNSUSING S REVIEW PAPER 15 to the cushion’s signals in order to unobtrusively measure an optical sensor mat was placed on the headrest of a massage the heart rate. Initially, low and high-frequency noises were chair. The participants were instructed to complete predefined suppressed using a band-pass finite impulse response (FIR) series of tasks, i.e., rest, cognitive test battery, and relaxing filter. Next, a cubing operation was applied to the filtered massage session. In this preliminary study, the analysis was signal to enhance the amplitude swing while keeping the signal done only during rest periods for a total of six minutes. A sign intact. Afterward, momentary upswing or downswing was band-pass filter was applied to the sensor data to remove removed by applying a moving average filter. Furthermore, low-frequency respiratory signals. Afterward, heart rate was the resultant signal was smoothed by utilizing the absolute computed using short-time Fourier transform. The proposed value and averaging over a predefined time window. At last, system achieved a relatively good agreement against the the J-peaks were recognized by using a cone detection and reference ECG. comparing to an adaptive threshold. The proposed system Chen et al.[87] reported the results of using the MFOS in achieved satisfactory results compared to the reference pulse a clinical trial for unobtrusive monitoring of heart rate and oximetry device. respiration during sleep. During the study, data were collected Chen et al.[78] studied the possibility of measuring blood from twenty-two subjects using the optical fiber sensor and pressure using ballistocardiography and photoplethysmography also from the standard polysomnography as a reference. In (PPG). The concept was to calculate the time delay between the beginning, large body movements were eliminated using the peaks of the ballistocardiography and the corresponding a moving time window. In which, a segment was identified PPG peaks. Ballistocardiogram signals were collected from five as a body movement if the difference between the maximum healthy subjects in a sitting position using a cushion integrated and the minimum in the moving window was larger than a with MFOS, whereas PPG signals were collected from a finger fixed threshold. Next, respiratory and heartbeat components pulse oximeter. Preliminary results have shown that blood were separated from the sensor’s signals using band-pass pressure might be measured using optical devices. However, filters of different cutoff frequencies. In the former, the signals the proposed approach was very challenging because it required were smoothed using a moving-average filter and hence the a calibration procedure for each subject prior to measurement. baseline was obtained by another moving-average filter of Lau et al.[79] evaluated the effectiveness of the MFOS for a larger window size. After subtracting the signals and the respiratory monitoring and respiratory gating in the magnetic baseline, they were further smoothed using Savitzky–Golay resonance imaging (MRI) environment. Respiratory gating method. Finally, local peaks were detected, and breathing rate is the process of reducing cardiorespiratory artifacts by syn- was computed. In the latter, all local peaks of the heartbeat chronizing magnetic resonance data acquisition to the cardiac signals were detected, and heart rate was computed accordingly. or respiratory cycles. Unlike electrical sensors, fiber-optic Consequently, incorrect heart rate values were eliminated by sensors are immune to electromagnetic and radio-frequency applying a histogram-based method, in which the group with interference. Twenty healthy subjects (10 males and 10 females) the highest occurrence was selected and reported as final heart were involved in the study and they underwent T2-weighted rate results. Results were promising. However, the proposed half-Fourier single-shot turbo spin-echo MRI of the liver with approach was prone to motion artifacts. synchronous breathing rate monitoring on a 1.5 Tesla magnetic Zhu et al.[88] proposed to measure heart rate using resonance scanner. The breathing rate was detected by applying ballistocardiogram signals collected from FBG sensor mat. The a band-pass filter and hence detecting local peaks in the time sensor mat consisted of three FBG sensor arrays or channels domain. This study presented that the MFOS were able to and each array contained six sensors. The arrays were located detect comparable breathing rate to the reference respiratory under the pillow, upper chest, and lower chest. In this study, ten bellows and produce liver MRI images of good diagnostic subjects were enrolled, and signals were collected during 20 quality compared to the navigator-acquired scans. Chen et al. minutes such as 10 minutes of supine posture and 10 minutes [80] reported related results using data collected from eleven of sideways posture. ECG signals were collected along with healthy subjects (6 males and 5 females) during MRI. the fiber-optic signals as a reference. The signal from each A similar study was provided by Dziuda et al.[81]. However, sensor array was transformed from time domain into cepstrum authors used FBG sensors rather than MFOS. Three healthy domain. After that, the signal from the six sensors of the same volunteers (2 males and 1 female) were included in the study arrays was fused by employing cepstrum. Finally, the heart and physiological data were collected during 95 minutes. Both rate was measured from the fused signal by recognizing peaks heart rate and breathing rate were measured by finding local in the cepstrum. This study demonstrated that the heart rate maxima after applying band-pass filters of different cutoff can be measured from distinct locations. However, the best frequencies to the sensor data. Similar to the MFOS, the results were achieved from sensor arrays at chest position. In FBG sensor did not introduce any artifacts into MRI images. another study, Zhu et al.[89] used the same system to compute Furthermore, the system achieved comparable results to the breathing rate and the system was tested against twelve subjects. reference devices, i.e., carbon electrodes and pneumatic bellows, Fajkus et al.[90] introduced to measure heart rate and respi- respectively. Dziuda et al.[82], [83], [84], [85] reported similar ration using FBG sensors encapsulated inside a polydimethyl- results using data collected during MRI examination. siloxane polymer (PDMS). The FBG sensors were embedded Zhu et al.[86] demonstrated the effectiveness of the MFOS within a thoracic elastic strap to record cardiorespiratory signals. for unobtrusive measurement of heart rate in a headrest position. In this preliminary analysis, the authors collected data from Three healthy individuals were enrolled in the study in which 10 individuals (6 males and 4 females) during few minutes. REVIEW PAPER 16

Heart rate and breathing rate were detected by adopting two effective because the training step should be repeated whenever methods, i.e., identifying the periodic cycles in the time domain the data collection protocol has been changed. Moreover, the and applying the FFT to obtain the dominant frequency. The ballistocardiogram morphology varies between and within proposed system achieved comparable results to the reference subjects, and the shape of the signal is highly dependent on ECG. However, it was susceptible to large body movements. subject’s postures, i.e., sleeping or sitting. Furthermore, the In another study, Fajkus et al.[91] assessed the effectiveness raw signal is noisy and nonstationary due to body movement, of using FBG sensor encapsulated inside a PDMS and FBG induced respiratory efforts, and the characteristics of the sensing sensor glued on a plexiglass pad for heart and respiratory rate system itself. monitoring. In this preliminary study, the authors collected data from 10 subjects (7 males and 3 females) and result shown that REFERENCES the FBG sensor encapsulated into PDMS was more accurate [1] E. Pinheiro, O. Postolache, and P. Girão, “Theory and developments in an than FBG sensor encapsulated in plexiglass pad. unobtrusive cardiovascular system representation: ballistocardiography,” Chethana et al.[92] reported the use of FBG sensor for The open biomedical engineering journal, vol. 4, p. 201, 2010. monitoring cardiac and breathing activities. Cardiorespiratory [2] I. Starr, A. Rawson, H. Schroeder, and N. 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MINUTES : Min , LABORATORY : Lab , CEPSTRUM RESPIRATORY RATE : : RR , CEPS , HEART RATE Outcome HR RR HR BR RR HR, RR HR, RR HR, RR HR HR HR HR, RR HR, RR HR, RR HR, RR HR, RR : HR , OURIERTRANSFORM F FEMALE : F Duration N/A N/A 5 Min N/A N/A N/A 95 Min 60 Min 19 Min 82 Min 6 Min Overnight 20 Min N/A 1 Min 60 Min , TIME - MALE : SHORT : M , STFT : Deployment Lab Lab Lab Lab MRI MRI MRI MRI MRI MRI Lab Hospital Lab Lab Hospital Lab NOT AVAILABLE : N/A . TABLE VII Subjects (M, F) N/A 9 N/A 5 N/A 5 N/A 10 M, 10 F 6 M, 5 F 2 M, 1 F 8 M, 4 F 1 M 6 M, 2 F 3 N/A 22 N/A 10 N/A 6 M, 4 F 2 M, 2 F 3 M, 3 F BASEDSENSORS - YDRAULIC H HOTOPLETHYSMOGRAPHYSYNCHRONIZATION :P Method Visually Visually Peak Detector PPG Sync Peak Detector Peak Detector Peak Detector Peak Detector Peak Detector Peak Detector STFT Peak Detector CEPS Peak Detector, FFT Visually Peak Detector PPG Sync , [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [86] [87] [88], [89] [90] [92] [93] BLOODPRESSURE : BP , SECONDS : Sec , HOURS UMMARYOFUNCONSTRAINEDMONITORINGOFVITALSIGNSUSING S REVIEW PAPER 18

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