Intelligent ECG Monitoring Tailored for Women’s Health
JanoLife’s Petal.today: AI-powered continuous ECG and physiological monitoring for women’s health, enabling earlier detection, personalized insights and proactive care.
Delivering continuous, AI-powered ECG insights designed specifically for women’s health.
CORE Approach
Petal.today is an AI-powered health monitoring platform designed to address long-standing gaps in women’s healthcare. We built a continuous, discreet, and clinically intelligent system that helps women better understand their heart health and physiological patterns—without disrupting daily life.
The Core Intent of Our Approach
Our approach focused on creating a women-first monitoring experience that prioritizes comfort, data accuracy, clinical relevance, and long-term scalability.
Unify health signals
Bring ECG, motion, breathing, and physiological signals together into a single, continuous monitoring experience tailored to women’s real-world health needs.
Transform signal intelligence
Replace noisy, unreliable wearable data with AI-driven signal processing that removes motion artifacts and delivers clinically meaningful ECG and health insights.
Preserve data trust
Maintain high data quality through automated quality checks, segmentation, and validation—ensuring insights are reliable for both users and clinicians.
Build scalable architecture
Enable large-scale women’s health monitoring with a secure, cloud-native platform designed to support a growing user base, advanced analytics, and future clinical integrations.
THE PROBLEM
Women’s health data is often incomplete, episodic, and poorly
contextualized—making it difficult to detect early risks, understand long-term patterns, and deliver timely care in real-world settings.
Intermittent monitoring
Traditional checkups and short-term wearables capture only brief snapshots, missing subtle but critical changes in women’s heart health and physiology over time.
Motion-distorted signals
Daily movement introduces noise into wearable ECG data, leading to inaccurate readings, false alerts, and reduced confidence in health insights.
Limited data trust
Without automated quality checks, poor or corrupted signals can influence analysis, making it hard for clinicians and users to rely on the data.
Lack of women-centric design
Most monitoring solutions are built on male-centric research models, failing to reflect women’s unique biology, comfort needs, and long-term monitoring requirements.
WORKFLOW
A structured, end-to-end workflow that transforms noisy, real-world wearable data into trusted, actionable insights, designed specifically for continuous women’s health monitoring.
Capture
real signals
Continuously collect ECG, motion, bioimpedance, and temperature data during daily activity, rest, and recovery.
Encrypt and transmit data to the cloud, using motion context to separate stationary and active segments.
Clean and
validate signals
Remove motion noise, assess signal quality, block corrupted data, and extract ECG, HRV, and breathing metrics.
Deliver
actionable data
Present validated trends and alerts through clear dashboards for proactive health decisions.
TECH INSIGHTS
This pipeline demonstrates how Petal.today transforms real-world wearable data, often affected by motion, noise, and signal loss—into clean, validated, and clinically meaningful health insights.
Raw signal
intake
ECG data is captured in real-world conditions, including noise, flatlines, and baseline drift.
Signal denoising
Advanced filtering removes motion artifacts and reconstructs clean ECG waveforms.
R-peak detection
Precise R-peaks are identified to enable accurate heart rate and HRV analysis.
Quality validation
Low-quality or corrupted segments are automatically detected and excluded.
Motion
fusion
Accelerometer data is combined with ECG to add real-time movement awareness.
Activity segmentation
Time segments are classified as stationary or non-stationary to isolate motion effects.
Breathing & HRV
Validated ECG data is used to derive breathing rate and heart-rate variability (HRV) metrics.
Fall
detection
Impact patterns and post-event inactivity are analyzed to detect falls and trigger alerts.
Result
Petal.today transformed noisy wearable data into trusted ECG
insights, enabling proactive, women-focused health monitoring and reliable clinical decision-making.
Improved confidence
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Clinical clarity
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Scalable platform
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Stronger trust
CASE STUDIES
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