Detection of fetal kicks using body-worn accelerometers during pregnancy: Trade-offs between sensors number and positioning

The effect of formal fetal movement counting on maternal psychological outcomes: A systematic review and meta-analysis

INTRODUCTION

Formally counting fetal movements in pregnancy is one of the oldest methods to assess fetal well-being. Although not routinely recommended in contemporary maternity care, due to a lack of evidence of its effectiveness, formal fetal movement counting is still practiced in many birth settings. Requesting women to formally count their fetal movements in a structured, objective way that can potentially improve maternal subjective outcomes such as worry or concern. The aim of this study was to evaluate the effect of formal fetal movement counting versus no formal counting, on maternal worry, concern or anxiety, and maternal–fetal attachment (MFA). Secondary outcomes were compliance with the intervention (counting method) and hospital admission/attendance for fetal activity concerns.

METHODS

CINAHL, MEDLINE and EMBASE were searched systematically for eligible studies from inception dates to June 2020, supplemented by searches of trial databases, grey literature and the reference lists of included studies. Randomized controlled and quasi-randomized trials were included in the review.

RESULTS

Nine studies reported across 15 publications were included involving 70824 pregnant women. The results showed that MFA levels were significantly higher in women who formally counted fetal movements than those who did not (standardized mean difference=0.72; 95% CI: 0.10–1.33, five studies, 1565 women). There were no differences between the groups in maternal anxiety or worry/concern outcomes. Attendance or admission rates for reduced fetal movements, or concern for fetal activity, did not differ between the groups (OR=1.36; 95% CI: 0.97–1.91, three studies, 1947 women). Compliance in completing fetal movement charts varied, ranging 45–90%, although definitions of compliance differed across studies, which may have affected rates.

CONCLUSIONS

This review has found that formal fetal movement counting in pregnancy has no detrimental effects on maternal psychological or emotional status and positively affects maternal–fetal attachment. Although current evidence does not support the use of formal fetal movement counting for improving perinatal outcomes, such as stillbirth and neonatal death, the results of this review are helpful for clinicians in discussing fetal movements in pregnancy and in discussing the optional methods available to women who may be advised to or choose to objectively assess fetal movements using a formal fetal movement counting method.

Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization

Fetal movement is an important clinical indicator to assess fetus growth and development status in the uterus. In recent years, a noninvasive intelligent sensing fetal movement detection system that can monitor high-risk pregnancies at home has received a lot of attention in the field of wearable health monitoring. However, recovering fetal movement signals from a continuous low-amplitude background that is heavily contaminated with noise and recognizing real fetal movements is a challenging task. In this paper, fetal movement can be efficiently recognized by combining the strength of Kalman filtering, time and frequency domain and wavelet domain feature extraction, and hyperparameter tuned Light Gradient Boosting Machine (LightGBM) model. Firstly, the Kalman filtering (KF) algorithm is used to recover the fetal movement signal in a continuous low-amplitude background contaminated by noise. Secondly, the time domain, frequency domain, and wavelet domain (TFWD) features of the preprocessed fetal movement signal are extracted. Finally, the Bayesian Optimization algorithm (BOA) is used to optimize the LightGBM model to obtain the optimal hyperparameters. Through this, the accurate prediction and recognition of fetal movement are successfully achieved. In the performance analysis of the Zenodo fetal movement dataset, the proposed KF + TFWD + BOA-LGBM approach's recognition accuracy and F1-Score reached 94.06% and 96.85%, respectively. Compared with 8 existing advanced methods for fetal movement signal recognition, the proposed method has better accuracy and robustness, indicating its potential medical application in wearable smart sensing systems for fetal prenatal health monitoring.

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. The FM signals are extracted and identified according to the clinical nature of the features hidden in the amplitude and waveform of the FM signals that fluctuate in duration. The system consists of four main stages: (i) FM signal preprocessing, (ii) maternal artifact signal preidentification, (iii) FM signal identification, and (iv) FM classification. Firstly, Kalman filtering is used to reconstruct the FM signal in a continuous low-amplitude noise background. Secondly, the maternal artifact signal is identified using an amplitude threshold algorithm. Then, an innovative dictionary learning algorithm is used to construct a dictionary of FM features, and orthogonal matching pursuit and adaptive filtering algorithms are used to identify the FM signals, respectively. Finally, mask fusion classification is performed based on the multiaxis recognition results. Experiments are conducted to evaluate the performance of the proposed FM detection system using publicly available and self-built accelerated FM datasets. The classification results showed that the orthogonal matching pursuit algorithm was more effective than the adaptive filtering algorithm in identifying FM signals, with a positive prediction value of 89.74%. The proposed FM detection system has great potential and promise for wearable FM health monitoring.

Novel non-invasive in-house fabricated wearable system with a hybrid algorithm for fetal movement recognition

Fetal movement count monitoring is one of the most commonly used methods of assessing fetal well-being. While few methods are available to monitor fetal movements, they consist of several adverse qualities such as unreliability as well as the inability to be conducted in a non-clinical setting. Therefore, this research was conducted to design a complete system that will enable pregnant mothers to monitor fetal movement at home. This system consists of a non-invasive, non-transmitting sensor unit that can be fabricated at a low cost. An accelerometer was utilized as the primary sensor and a micro-controller based circuit was implemented. Clinical testing was conducted utilizing this sensor unit. Two phases of clinical testing procedures were done and during the first phase readings from 120 mothers were taken while during the second phase readings from 15 mothers were taken. Validation was done by conducting an abdominal ultrasound scan which was utilized as the ground truth during the second phase of the clinical testing procedure. A clinical survey was also conducted in parallel with clinical testings in order to improve the sensor unit as well as to improve the final system. Four different signal processing algorithms were implemented on the data set and the performance of each was compared with each other. Out of the four algorithms three algorithms were able to obtain a true positive rate around 85%. However, the best algorithm was selected on the basis of minimizing the false positive rate. Consequently, the most feasible as well as the best performing algorithm was determined and it was utilized in the final system. This algorithm have a true positive rate of 86% and a false positive rate of 7% Furthermore, a mobile application was also developed to be used with the sensor unit by pregnant mothers. Finally, a complete end to end method to monitor fetal movement in a non-clinical setting was presented by the proposed system.

Assessing fetal movements in pregnancy: A qualitative evidence synthesis of women's views, perspectives and experiences

Background

Raising awareness of the importance of fetal movements (FMs) and advising women on the appropriate action to take if they experience reduced FMs, is important for minimising or avoiding adverse perinatal outcomes. To gain insight and understanding of women’s perspectives of assessing FMs in pregnancy, we conducted a qualitative evidence synthesis.

Methods

A qualitative evidence synthesis using thematic synthesis was conducted. Studies were eligible if they included pregnant women who were at least 20 weeks gestation and reported qualitative data from women on assessing FMs in pregnancy. MEDLINE, CINAHL, EMBASE, PsycINFO and Social Science Citation Index, from inception to July 2020, were searched. The methodological quality of included studies was assessed by at least two reviewers using an Evidence for Policy and Practice Information (EPPI)-Centre quality assessment tool. Data synthesis, using the Thomas and Harden framework, involved line by line coding of extracted data, establishing descriptive themes, and determining analytical themes. Confidence in the findings was assessed using GRADE CER-Qual.

Results

Nine studies, involving 2193 women, were included in the review. The methodological quality of the studies was overall generally high. The synthesis revealed three dominant themes, and seven sub-themes that reflected women’s perspectives of assessing FMs in pregnancy. These were; 1) How women engage with FMs, with subthemes of informal engagement, formal engagement, and strategies to stimulate FMs; 2) ‘ … like a feather inside my belly’ - articulating and describing FMs, with sub-themes of sensations associated with FMs and timing and frequency of FMs; and 3) FMs and help/health seeking, with sub-themes of information sources and interacting with healthcare professionals. Confidence in the findings was either high or moderate, although two findings were rated low confidence and one very low.

Conclusion

This qualitative evidence synthesis reveals that women informally engage with FMs during pregnancy. Women commonly adopt strategies to stimulate FMs when concerned. The use of the internet was a common source of obtaining information regarding FMs. Women require better support when contacting healthcare professionals about FMs. As only three of the nine included studies were exclusively qualitative in design, further qualitative studies exploring women’s perspective of assessing FMs in pregnancy are required.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-03667-y.

A Fetal Movement Simulation System for Wearable Vibrational Sensors

This paper introduces a low-cost phantom system that simulates fetal movements (FMVs) for the first time. This vibration system can be used for testing wearable inertial sensors which detect FMVs from the abdominal wall. The system consists of a phantom abdomen, a linear stage with a stepper motor, a tactile transducer, and control circuits. The linear stage is used to generate mechanical vibrations which are transferred to the latex abdomen. A tactile transducer is implemented to add environmental noise to the system. The system is characterized and tested using a wireless sensor. The sensor recordings are analyzed using time-frequency analysis and the results are compared to real FMVs reported in the literature. Experiments are conducted to characterize the vibration range, frequency response, and noise generation of the system. It is shown that the system is effective in simulating the vibration of fetal movements, covering the full frequency and magnitude ranges of real FMV vibrations. The noise generation test shows that the system can effectively create scenarios with different signal-to-noise ratios for FMV detection. The system can facilitate the development of fetal movement monitoring systems and algorithms.

A Novel Fetal Movement Simulator for the Performance Evaluation of Vibration Sensors for Wearable Fetal Movement Monitors

Fetal movements (FM) are an important factor in the assessment of fetal health. However, there is currently no reliable way to monitor FM outside clinical environs. While extensive research has been carried out using accelerometer-based systems to monitor FM, the desired accuracy of detection is yet to be achieved. A major challenge has been the difficulty of testing and calibrating sensors at the pre-clinical stage. Little is known about fetal movement features, and clinical trials involving pregnant women can be expensive and ethically stringent. To address these issues, we introduce a novel FM simulator, which can be used to test responses of sensor arrays in a laboratory environment. The design uses a silicon-based membrane with material properties similar to that of a gravid abdomen to mimic the vibrations due to fetal kicks. The simulator incorporates mechanisms to pre-stretch the membrane and to produce kicks similar to that of a fetus. As a case study, we present results from a comparative study of an acoustic sensor, an accelerometer, and a piezoelectric diaphragm as candidate vibration sensors for a wearable FM monitor. We find that the acoustic sensor and the piezoelectric diaphragm are better equipped than the accelerometer to determine durations, intensities, and locations of kicks, as they have a significantly greater response to changes in these conditions than the accelerometer. Additionally, we demonstrate that the acoustic sensor and the piezoelectric diaphragm can detect weaker fetal movements (threshold wall displacements are less than 0.5 mm) compared to the accelerometer (threshold wall displacement is 1.5 mm) with a trade-off of higher power signal artefacts. Finally, we find that the piezoelectric diaphragm produces better signal-to-noise ratios compared to the other two sensors in most of the cases, making it a promising new candidate sensor for wearable FM monitors. We believe that the FM simulator represents a key development towards enabling the eventual translation of wearable FM monitoring garments.