The Continuous Health Monitoring Gap
Most health monitoring is episodic — a clinic visit, a single reading, a snapshot in time. For chronic disease management, preventive care, and early detection, what matters is the pattern across days and weeks, not a single data point. The gap between clinical visits leaves users without visibility into the physiological trends that define long-term health outcomes.
AyuRing addresses this gap by moving continuous, multi-parameter health monitoring from clinical environments to everyday life. Worn on the finger — one of the body's most reliable sites for optical sensing — AyuRing captures a structured dataset of physiological parameters around the clock, feeding trend-based insights into a connected mobile application designed for both user awareness and clinical-grade reference.
11 Monitored Parameters
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Heart Rate
Continuous PPG-based heart rate monitoring — tracking beats per minute in real time and flagging deviations from personalised baseline ranges.
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ECG
Single-lead electrocardiography for cardiac rhythm assessment — enabling detection of irregularities such as atrial fibrillation outside of clinical settings.
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SpO₂
Blood oxygen saturation monitoring — providing continuous insight into respiratory and circulatory efficiency, particularly relevant for respiratory condition management.
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Heart Rate Variability (HRV)
Autonomic nervous system health indicator derived from beat-to-beat interval variation — reflecting recovery status, stress load, and cardiovascular resilience.
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Stress
Physiological stress index derived from HRV and multi-parameter pattern analysis — quantifying stress burden across the day and correlating it with activity and sleep data.
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Sleep Stage Analysis
Multi-stage sleep architecture tracking — distinguishing light, deep, and REM sleep to support sleep quality assessment and recovery optimisation.
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Activity Tracking
Step count, movement classification, and activity intensity monitoring — providing a structured record of daily physical activity patterns and caloric expenditure estimates.
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Skin Temperature
Continuous surface temperature trend monitoring — detecting fever onset, ovulation patterns, and physiological shifts linked to illness or environmental stress.
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Range-Based Glucose Estimates
Non-invasive, trend-based glucose estimation using multi-sensor fusion — designed to support awareness of glycaemic patterns rather than replace clinical glucose measurement.
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Range-Based Creatinine Estimates
Wearable-derived renal health indicator via optical and physiological sensing — providing a non-invasive signal for renal function trend monitoring between clinical assessments.
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Range-Based Blood Pressure Estimates
Continuous blood pressure trend estimation using pulse transit time and multi-parameter modelling — enabling longitudinal BP pattern awareness without cuff-based measurement.