Maternitec
Maternitec is developing FeMo, a wearable device that passively tracks fetal movements overnight during the third trimester. The system uses a soft band with vibration sensors, AI-based analysis, and a mobile app to establish each baby's unique activity baseline and flag sustained changes. It aims to provide objective data for expectant mothers and clinicians between antenatal appointments.
- Healthcare Technology
- Medical Devices
- Digital Health
- Internet of Things
- Artificial Intelligence
- Hardware
- Software Only
- Consumer Electronics
Funding
Founders
Founder details are not available yet.
Product
Problem
Antenatal care only checks on the baby during scheduled appointments, which can be weeks apart, leaving no objective information on fetal wellbeing in between. Decreased fetal movement is associated with higher risk of late stillbirth, yet current monitoring relies on subjective maternal perception of movement, which is highly variable and lacks a reliable record or baseline for comparison.
Solution
Maternitec is developing FeMo, a wearable device designed to provide objective, continuous monitoring of fetal movements overnight. The system uses a soft band with passive vibration sensors worn during sleep, with recordings securely transferred to the cloud for AI-based movement analysis. The mobile app presents nightly trends of active and resting time, comparing each baby against its own established baseline rather than population averages. FeMo is being designed to notify users when a sustained change from the baby's baseline is detected, prompting them to contact their healthcare provider.
Target Audience
Primary customers are expectant mothers in their third trimester who want objective data about their baby's movements, as well as healthcare providers and maternity units seeking to support home-based fetal wellbeing monitoring.
Features
- Passive vibration sensors in a soft band that require no active user input or gel
- Secure cloud infrastructure for transferring and processing overnight recordings
- AI-based algorithms being developed to identify fetal movements in each night's recording
- Mobile app showing nightly activity trends and comparisons to the baby's own baseline
- Notification system designed to flag sustained changes from the baby's typical pattern
- Research based on two completed studies with 100 participants and over 12,000 hours of overnight recordings