Why the study?
Traditional methods of foetal movement monitoring are limited by subjectivity and lack of reliability, especially in continuous and home-based settings.
Automated fetal movement monitoring technologies, enhanced by AI and machine learning, offer a promising alternative to subjective traditional methods for continuous and home-based prenatal care.
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Automated FM monitoring technologies merit prospective validation; leaves open routine clinical integration.
Foetal movement (FM) is widely regarded as a key indicator of foetal health, offering insights into neurological development and the overall physiological state of the foetus. Traditional methods of FM monitoring, such as maternal perception, manual counting, and ultrasound-based methods, are limited by subjectivity and lack of reliability, especially in continuous and home-based settings. Recent advances in technology have introduced automated methods of FM detection, ranging from accelerometry to non-invasive foetal electrocardiography, each with unique advantages and challenges. This review presents a comprehensive overview of current FM monitoring technologies, focusing on their principles, applications, and limitations. Additionally, we discuss the importance of standardised datasets and uniform benchmarking, which are essential to validating and comparing emerging technologies. Finally, we explore the role of advanced signal processing, artificial intelligence and machine learning in enhancing FM detection accuracy, with the potential to expand foetal monitoring into the home and enable more personalised prenatal care.
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Mikolasova et al. (2026) studied this question.
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