Variable-length accelerometer features and electromyography to improve accuracy of fetal kicks detection during pregnancy using a single wearable device

Altini, Marco; Rossetti, Elisa; Rooijakkers, Michiel; Penders, Julien; Lanssens, Dorien; Grieten, Lars; Gyselaers, Wilfried

doi:10.1109/bhi.2017.7897245

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…They proposed a system that combined data from accelerometers and electromyography (EMG). The system drastically reduced the false-positive kick detection [ 104 ].…”

Section: Sensing Devices In Healthcarementioning

confidence: 99%

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

et al. 2022

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Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

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“…They proposed a system that combined data from accelerometers and electromyography (EMG). The system drastically reduced the false-positive kick detection [ 104 ].…”

Section: Sensing Devices In Healthcarementioning

confidence: 99%

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

et al. 2022

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“…However, the sensitivity of FM varies greatly from pregnant women [12], and it is challenging to monitor FM in the long term by subjective judgment. In recent years, wearable health monitoring devices had become a hot spot of research in the biomedical field, and the use of wearable acceleration sensors and modern digital signal processing techniques to achieve automatic recognition of FM has received widespread attention from researchers from all walks of life [13][14][15][16][17][18][19][20][21][22][23][24]. e accelerometers are small, inexpensive, noninvasive, sensitive, and stable and have become the ideal solution for noninvasive FM recognition.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, some researchers have used traditional machine learning classifiers to train and predict the extracted raw acceleration time-and frequency-domain feature signals that aim to distinguish the FM signal class of other noisy signal classes [17,[21][22][23][24]. Vullings and Mischi [17] proposed a method of noninvasive monitoring of FM by using TF characteristics.…”

Section: Introductionmentioning

confidence: 99%

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

Liang

Peng

2021

Journal of Healthcare Engineering

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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.

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“…In recent years, with the advancement of microelectronic technology as well as the development in signal processing, automatic detection of fetal movements by using accelerometers and advanced signal processing technologies has gained a lot of attention [9,10,11,12,13,14,15,16,17,18,19]. When contacted with the maternal abdominal wall, a movement of fetal body parts with sufficient force generates vibrations that could be detected by one or a set of accelerometers placed on the surface of the abdomen.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Then, based on the extracted features, signal classification was performed to identify fetal movement signals from other signals. One point worth mentioning is that some previously published papers proposed an additional sensor placed on the mothers thigh or back to detect and eliminate maternal artifacts [11,14,15]. However, until now there is still no standard for the optimal placement of this reference sensor, and the integration of this additional sensor involves additional complexity to the monitoring system and thus brings limits to the use of this technique in real world applications.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Wearable System for In-Home and Long-Term Assessment of Fetal Movement

Zhao

Zeng

Koehl

et al. 2020

IRBM

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Objectives: This paper presents a novel wearable system for in-home and long-term fetal movement monitoring on a reliable and easily accessible basis. Material and methods: The system mainly consists of four accelerometers for fetal movement signal acquisition, a microcontroller for signal processing and an Android-based device interacting with the microcontroller via Bluetooth Low Energy (BLE), providing the mother with information related to the fetal movement in an intelligible way. Results: The proposed system can deliver reliable results with a specificity of 0.99 and a sensitivity of 0.77 for fetal movement time series signal classification. Conclusion: The proposed wearable system will provide a good alternative to optimize the use of medical professionals and hospital resources, and has potential applications in the field of e-Health home care. Besides, the fetal movement acceleration signals acquired with volunteers (pregnant women) helps establish an initial database for future medical analysis of sensor-recorded fetal behaviors.

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Variable-length accelerometer features and electromyography to improve accuracy of fetal kicks detection during pregnancy using a single wearable device

Cited by 13 publications

References 11 publications

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology

A Wearable System for In-Home and Long-Term Assessment of Fetal Movement

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