Following interviews and co-design workshops with 50 Australian mothers and mothers-to-be—using tactile activities like matching photos to feelings, crafting “baby mood” mobiles, and jelly-bean jars to explore emotional capacity—we translated those human insights into physiological signals a wearable can observe every day.

The result is a microcontroller-powered wearable, paired with a mobile app, that passively captures indicators related to stress load, sleep quality, psychophysiological arousal, and daily rhythms. The system blends opt-in mood check-ins with continuous biometrics such as heart-rate variability, skin conductance, temperature, and activity. Rather than diagnosing, it provides gentle insights like “Your recovery is trending low” or “Sleep has been fragmented this week,” guiding users toward self-care or professional support.

How the Wearable Works

• Photoplethysmography (PPG): Monitors heart rate and HRV to reflect stress and recovery patterns.
• Electrodermal Activity (EDA): Detects changes in skin conductance as an indicator of arousal or anxiety.
• Skin Temperature: Tracks circadian rhythm, hormonal fluctuations, and sleep quality.
• 3-Axis Accelerometer: Monitors movement, restlessness, and sleep fragmentation.
• Respiration Rate & Light Exposure: Provide context for relaxation levels and circadian balance.

Data processing happens on-device before syncing securely to the app, where users can view simple visual feedback about their wellbeing trends. The wearable uses personal baselines established over 1–2 weeks to detect deviations—reducing false alarms and focusing on individual change, not averages.

Device Design & Placement

The first prototypes took the form of a comfortable wrist-worn band—ideal for continuous monitoring while caring for an infant. It combines stainless electrodes for EDA with an optical PPG sensor for HRV, integrated into a soft, hypoallergenic strap. Alternate configurations such as rings and upper-arm bands were also tested for sleep comfort and accuracy.

Validation testing included signal quality sessions during real-world caregiving activities—feeding, rocking, and sleep routines—paired with psychometric scales like the EPDS and PSS-10. These correlations informed algorithm calibration and practical UX choices like one-handed operation and discreet feedback cues.