You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Ballistocardiography and Seismocardiography: A Review of Recent Advances 
Chapter Title:  B. Noise and Interference Reduction 
1. Abstract of Cited Paper (BIB001):  Ballistocardiography is a non-invasive technique for evaluating cardiovascular health. This note presents an ac-bridge amplifier for low-noise ballistocardiogram (BCG) recording from a modified weighing scale. The strain gauges in a commercial scale were excited by an ac source-square or sine wave-and the differential output voltage resulting from the BCG was amplified and demodulated synchronously with the excitation waveform. A standard BCG amplifier, with a simple dc-bridge excitation, was also built and the performance was compared to both the square- and sine-wave excited ac-bridge amplifiers. The total input-referred voltage noise (rms) integrated over the relevant BCG bandwidth of 0.3-10 Hz was found to be 30 nV (square wave source) or 25 nV (sine-wave source) for the ac-bridge amplifier and 52 nV for the standard amplifier: an improvement of 4.8 dB or 6 dB, respectively. These correspond to input-referred force noise (rms) values of 5 mN, 4 mN and 8.3 mN. The improvement in SNR was also observed in recorded waveforms from a seated subject whose BCG signal was measured with both dc- and ac-bridge circuits. 
2. Abstract of Cited Paper (BIB002):  Ballistocardiography (BCG) is a non-invasive technique used to measure the ejection force of blood into the aorta which can be used to estimate cardiac output and contractility change. In this work, a noise sensor was embedded in a BCG measurement system to detect excessive motion from standing subjects. For nine healthy subjects, the cross-correlation of the motion signal to the BCG noise--estimated using a simultaneously acquired electrocardiogram and statistics of the BCG signal--was found to be 0.94 and 0.87, during periods of standing still and with induced motion artifacts, respectively. In a separate study, where 35 recordings were taken from seven subjects, a threshold-based algorithm was used to flag motion-corrupted segments of the BCG signal using only the auxiliary motion sensor. Removing these flagged segments enhanced the BCG signal-to-noise ratio (SNR) by an average of 14 dB (P < 0.001). This integrated motion-sensing technique addresses a gap in methods available to identify and remove noise in standing BCG recordings due to movement, in a practical manner that does not require user intervention or obtrusive sensing. 
3. Abstract of Cited Paper (BIB003):  An adaptive noise canceller was used to reduce the effect of floor vibrations on ballistocardiogram (BCG) measurements from a modified electronic bathroom scale. A seismic sensor was placed next to the scale on the floor and used as the noise reference input to the noise canceller. BCG recordings were acquired from a healthy subject while another person stomped around the scale, thus causing increased floor vibrations. The noise canceller substantially eliminated the artifacts in the BCG signal due to these vibrations without distorting the morphology of the measured BCG. Additionally, recordings were obtained from another subject standing inside a parked bus while the engine was running. The artifacts due to the vibrations of the engine, and the other vehicles moving on the road next to the bus, were also effectively eliminated by the noise canceller. The system with automatic floor vibration cancellation could be used to increase BCG measurement robustness in home monitoring applications. Additionally, the noise cancellation approach may enable BCG recording in ambulances-or other transport vehicles-where noninvasive hemodynamic monitoring may otherwise not be feasible. 
4. Abstract of Cited Paper (BIB004):  The ballistocardiogram (BCG) is a measure of the reaction force of the body to cardiac ejection of blood. A variety of systems can be used for BCG detection, including beds, tables, chairs, and weighing scales. Weighing scales, in particular, have several practical advantages over the alternatives: low cost, small size, unobtrusiveness, and familiarity to the user; one disadvantage is that the subject must stand during the recording, rather than sit or lay supine, resulting in a higher susceptibility to motion artifacts in the measured signal. This paper evaluates the electromyogram (EMG) signal acquired from the feet of the subject during BCG recording as a noise reference for standing BCG measurements. As a subject moves while standing on the scale, muscle contractions in the feet are detected by the EMG signal, and used to flag segments of the BCG signal that are corrupted by elevated noise. For the purposes of evaluating this method, estimates of the BCG noise-to-signal ratio (NSR) were independently calculated with an ensemble average method, using the R-wave of a simultaneously-acquired chest ECG as a timing reference. The linear correlation between EMG power alone and BCG NSR from 14 subjects was found to be moderate ( r = 0.58, F-statistic p -value 0.05); combined with body-mass index (BMI), multiple linear regression yielded a stronger correlation ( r = 0.73, F -statistic p-value = 0.01). Additionally, an example usage of the lower-leg EMG for improving BCG measurement robustness is provided. 
5. Abstract of Cited Paper (BIB005):  Embedded wireless sensors are important components of mobile distributed computing networks, and one of the target applications areas is health care. The preservation of mobility for senior citizens is one of the key issues in maintaining an independent lifestyle. Thus health technologies inside a car can contribute both to safety issues (supervision of driver fitness) as well as healthcare issues by monitoring vitals signs imperceptibly. In this paper, three embedded measurement techniques for non-contact monitoring of vital signals have been investigated. Specifically, capacitive electrocardiogram (cECG) monitoring, mechanical movement analysis (ballistocardiogram, BCG) using piezo-foils and inductive impedance monitoring were examined regarding their potential for integration into car seats. All three sensing techniques omit the need for electroconductive contact to the human body, but require defined mechanical boundary conditions (stable distances or, in the case of BCG, frictional connection). The physical principles of operation, the specific boundary conditions regarding automotive integration and the results during wireless operation in a running car are presented. All three sensors were equipped with local intelligence by incorporating a microcontroller. To eliminate the need for additional cabling, a wireless Bluetooth communication module was added and used to transmit data to a measurement PC. Finally, preliminary results obtained during test drives on German city roads and highways are discussed. 
6. Abstract of Cited Paper (BIB006):  Abstract 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. 
7. Abstract of Cited Paper (BIB007):  We report on the feasibility of standing ballistocardiogram (BCG) measurements recorded in a microgravity environment. A clinically-tested BCG monitoring scale was adapted for parabolic flight for the microgravity measurements. Upon completion of this flight campaign, the BCG scale was shown to make measurements in micro-g and one-g environments-which is a first demonstration for a standing BCG system. This screening experiment demonstrated proof-of-concept attributes of the hardware design necessary for future characterization studies with multiple subjects. This scale-based BCG system is proposed as a practical device for hemodynamic monitoring for astronauts in Earth, Lunar, Martian, orbital, and interplanetary environments.

Literature Review:
Several sources of noise and interference can potentially corrupt BCG and SCG measurements taken using modern systems. These include sensor and circuit noise BIB002 , motion artifacts BIB001 , BIB007 , BIB003 ,  , and floor vibrations (for standing BCG measurements) BIB004 . Both BCG and SCG represent low-level signals that contain very low-frequency information-this can lead to problems with flicker (1/f) noise in the sensor interface circuit corrupting the measurements. Furthermore, many diseased subjects, and elderly subjects, have smaller signal amplitudes compared to the healthy young population  . The sensor and circuit noise were characterized and reduced for weighing-scale-based BCG systems using an ac-bridge amplifier approach BIB002 . This approach led to a SNR improvement of 6 dB. For ambulatory and standing subjects, motion artifacts present the greatest potential obstacle to achieving reliable measurements. Unlike bed or chair systems, where the subject stays generally still for the measurement, postural sway, or ambulation can create unwanted peaks or distortion in the measured signals. Motion artifact detection for standing BCG measurements was accomplished using auxiliary sensors as noise references; then, gating the BCG signal based on the detection of excessive noise BIB003 , BIB005 . In one study, the noise reference was an extra strain gauge added to the scale to detect postural sway BIB003 . In another study, the rms power of the electromyogram signal from the feet, indicating the presence of increased muscle contractions due to excessive movement, was used as a noise gate for the BCG BIB005 . Pandia et al. presented preliminary methods for cancelling motion artifacts in SCG signals from walking subjects, improving overall heartbeat detection  . Di Rienzo et al. used an automatic selection of movement-free data segments from daily recordings of SCG signals from ambulant subjects, followed by an ECG triggered ensemble averaging to reduce signal noise BIB007 . This enabled, for the first time, the assessment of systolic time intervals profiles during normal daily living. BCG measurements taken in a direction orthogonal to the plane of the floor can potentially be corrupted by floor vibrations-this can particularly pose a challenge for measurements taken on a vehicle BIB006 or plane BIB008 . Walter et al. instrumented the seat of a car with an EMFi mat to measure the BCG, aiming to use the information to monitor driver fitness BIB006 . However, with the engine turned on, the BCG was corrupted by vibration artifacts and rendered unusable. Inan et al. used an auxiliary sensor for vibration detection and adaptive noise cancellation to cancel floor vibration artifacts in the BCG measurement BIB004 . In this study, high-quality BCG measurements were successfully demonstrated from a subject standing on a bus with the engine turned on and idling. Additionally, it was observed that low-noise SCG waveforms could be obtained in a subject sitting in the metro, while a train was going by, with the above mentioned ensemble averaging approach BIB007 .