Remote Patient Monitoring
Device-agnostic integration of medical grade and consumer wearables, continuous physiological data streaming, AI driven signal quality scoring, and automated safety alerting, unified under a single regulatory ready data model.
Demonstration values, not patient data
Simulated single-lead ECG trace with oscillating vital sign values for illustrative purposes. Clinical accuracy of production data depends on paired medical-grade device integration.
Signals and Technologies We Support
Cardiovascular
Continuous single lead electrocardiogram. Photoplethysmography derived heart rate and heart rate variability. Pulse oximetry for peripheral oxygen saturation. Blood pressure through integrated clinical grade devices.
Neurological
Continuous EEG through reusable dry-electrode systems, planned for the PT-EPI-DCT-002 study. Seizure event capture and structured investigator review workflows. Sleep architecture analysis from EEG and accelerometry.
Metabolic
Continuous glucose monitoring is designed for; manufacturer API authorisations are in progress and alternatives are under evaluation. Respiratory rate from PPG-derived and rPPG signals. Energy expenditure and metabolic activity through accelerometry.
Autonomic and Dermal
Electrodermal activity for stress and autonomic response quantification. Skin temperature with clinical grade accuracy. Respiratory waveform through multi sensor fusion.
Activity and Behavioral
Triaxial accelerometry for step count, gait metrics, and activity classification. Sleep staging and sleep quality scoring. Fall detection and posture recognition.
Contactless Monitoring
Camera based contactless vital sign extraction through remote photoplethysmography. Heart rate, respiratory rate, and stress indicators obtained from standard smartphone video without wearable hardware. Designed for decentralized studies where device burden must be minimized, and for remote screening and telehealth workflows.
Continuous Monitoring, Anywhere
Turn every patient into a connected data source with broad device integration.
Broad Device Integration
Unified integration layer for clinical grade and consumer wearables, normalizing heterogeneous device data into standardized FHIR R4 observations so any sensor in our catalog can contribute to the same analysis.
Real-Time Data Streaming
Sub-second data ingestion with edge processing for bandwidth optimization. Continuous vital signs flow into your trial database without patient intervention.
AI Anomaly Detection
Machine learning models trained on clinical grade physiological data detect anomalies in real-time. Flag arrhythmias, desaturation events, and sudden vital sign changes automatically.
Automated Safety Alerts
Configurable alert cascades notify investigators, study coordinators, and medical monitors when patient data crosses safety thresholds. Tiered escalation with acknowledgment tracking.
Data Normalization
Heterogeneous device data normalised into standardised FHIR R4 Observation resources. Compare heart rate from a consumer wearable with a medical-grade device in the same analysis.
Patient Engagement
Patient-facing dashboards show health trends and encourage adherence. Gamification elements, achievement badges, and personalized health insights boost device wear compliance.
Signal Quality Scoring
Automated signal quality assessment for every data point. Flag motion artifacts, sensor disconnections, and low-confidence readings before they contaminate your dataset.
A Networked Sensor Fabric
Clinical-grade instruments and consumer wearables flow into one unified device layer. Hover a category to highlight its place in the mesh.
Device Categories
Continuous EEG through reusable dry-electrode systems, planned for the PT-EPI-DCT-002 study.
Continuous glucose monitoring is designed for; manufacturer API authorisations are in progress and alternatives are under evaluation.
Hover a category to highlight its corresponding node in the 3D mesh, or vice versa.
How It Works
From device provisioning to real-time insights in three steps.
Connect Devices
Select devices from our integration catalog or bring your own. Patients pair devices through the mobile app with guided setup, no technical expertise required.
Stream & Monitor
Continuous data flows from patient devices to your trial database. AI models analyze streams in real-time, flagging anomalies and triggering safety alerts automatically.
Analyze & Act
Normalized, quality-scored data feeds directly into your EDC and analytics modules. Generate regulatory ready device data summaries and safety narratives.
See it in action
eHealth CTMS + RPM — Medication Reminder
Questions and answers
Remote patient monitoring questions
Which devices does Vitruvian Shield RPM support?
The Analog Devices Study Watch is integrated natively and available. The Dexcom G7 CGM, Corsano CardioWatch and X-trodes dry-electrode EEG are connected through Thryve aggregation and activated per study. Camera-based contactless vital signs through remote photoplethysmography are in validation.
Which physiological signals can be collected?
Cardiovascular signals (single-lead ECG, PPG-derived heart rate and heart rate variability, pulse oximetry, blood pressure through integrated devices), neurological signals (continuous dry-electrode EEG planned for the PT-EPI-DCT-002 study), metabolic, autonomic and dermal signals, activity and sleep from accelerometry, and contactless vital signs from smartphone video.
How is signal quality handled?
Every data point receives an automated signal-quality assessment that flags motion artefacts, sensor disconnections and low-confidence readings before they enter the dataset. Device data is normalised into HL7 FHIR R4 Observation resources so readings from different devices can be compared in one analysis.
Can participants be monitored without visiting a site?
Yes. Participants pair their devices through the mobile app with guided setup, data streams continuously to the study team, and configurable alert cascades notify investigators, coordinators and medical monitors when a reading crosses a safety threshold.
Is the RPM module a CE-marked medical device?
Not yet. The platform is being developed as software as a medical device, Class IIb under EU MDR, with CE marking targeted for 2028 through a notified body. Current use is in research settings under each study's protocol and ethics approval.
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