Maternal and perinatal health remains a priority in global public health, with wearable sensors emerging as a critical tool for continuous, objective physiological and behavioural monitoring outside the clinic. Traditional antenatal care relies on intermittent visits and relatively quick assessments; by contrast, wearable technology, such as our VU-AMS Core, enables ambulatory, longitudinal data collection across gestation and into the postpartum period. Recent publications have shown that a range of wearable sensors have been used to track both maternal and fetal signals, such as fetal heart rate, maternal heart activity, body motion, and physical activity levels. These devices have been applied in controlled and free-living environments, although broader validation in daily life remains an ongoing research need.  Studies indicate that wearables can capture meaningful trends in pregnancy physiology and behaviour. For example, continuous sensor data has been shown to align with expected pregnancy-related hormonal changes and patterns in resting heart rate and sleep across gestation and postpartum, suggesting potential for early risk assessment of adverse outcomes. Patient and provider perspectives further support the adoption of technology: surveys among pregnant women demonstrate strong acceptability of wearable ECG-like devices for monitoring maternal and fetal health, with many expressing a willingness to use them throughout pregnancy, including overnight. Patients reported comfort with data sharing and openness to behaviour modification based on sensor feedback. Beyond heart-centric measures, wearable research also encompasses activity, sleep, and physical behaviour. A recent prospective study using wearable activity trackers across the perinatal period found that while overall activity levels during pregnancy were maintained, sleep quality changed significantly by trimester, with physical activity correlating positively with sleep outcomes. Research is not limited to simple tracking: multimodal sensor systems and advanced data processing, including AI and machine learning, are increasingly integrated into pregnancy monitoring frameworks. These approaches integrate data from ECG, inertial sensors, and other biosignals to capture physical activity and detect subtle changes, improving interpretation and enabling earlier, more targeted interventions. Some challenges remain despite this. Most wearable studies to date employ relatively small sample sizes and limited continuous naturalistic monitoring. More rigorous, large-scale validation is needed to confirm reliability across diverse populations and to translate findings into actionable clinical insights. As maternal health research increasingly values real-world, high-resolution physiological data, ambulatory monitors like ours will likely play a growing role in both research and patient-focused care. Their potential to enable early detection of deviations from healthy pregnancy trajectories and support personalised maternal care highlights a pivotal moment in health technology innovation.