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MedTech & Wearable Health

On-device diagnostics. No cloud required.

Deploy real-time ECG classification, SpO2 monitoring, and vital sign analysis directly on your medical device MCU. Offline, deterministic, with hardware-level IP protection.

The Challenge

Medical AI cannot depend on the cloud.

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Latency Kills

Cloud-based analysis adds seconds of delay. For arrhythmia detection, a delayed alert can be the difference between intervention and incident.

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Patient Data Privacy

Transmitting raw ECG or health signals to external servers raises GDPR, HIPAA, and patient consent issues. On-device processing eliminates the risk entirely.

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Battery & Connectivity

Continuous BLE/WiFi transmission drains battery life. Wearable devices need to last days, not hours. Offline inference cuts power consumption dramatically.

The Solution

Clinically relevant AI on an 11 KB model.

Luviner's Edge V3 engine trains Neural Networks on your physiological data and compiles them to pure C for ARM Cortex-M. Deterministic, quantized, no floating-point rounding — ready for regulatory validation.

97.3%
ECG classification accuracy
11 KB
Model size in flash
0.6 ms
Per-beat inference
100%
Offline operation
Applications

From ECG to SpO2. On-device.

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ECG Arrhythmia Detection

5-class AAMI classification (N, S, V, F, Q) in real-time. 97.3% accuracy on MIT-BIH benchmark, 95.8% sensitivity on ventricular ectopic beats.

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SpO2 & Vital Signs

Continuous blood oxygen saturation monitoring with anomaly detection. Alert on desaturation events without cloud connectivity.

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Respiratory Analysis

Respiratory rate and pattern classification from IMU or chest-band sensors. Detect apnea events on-device during sleep monitoring.

Regulatory Ready

Built for compliance.

Deterministic Output

Integer arithmetic produces identical outputs for identical inputs. No floating-point rounding variance — critical for FDA/EU MDR validation.

Hardware-Locked Binary

UID binding ensures the validated software runs only on certified hardware. Prevents unauthorized deployment on unqualified devices.

Versioned & Traceable

Each compiled binary is unique and watermarked. Full traceability from training data to deployed firmware for audit trails.

How It Works

From physiological data to certified device.

Prepare Dataset

Export labeled ECG/SpO2/IMU data as CSV. Use clinical datasets or your own recordings.

Train & Validate

Upload to Luviner. Edge V3 trains, quantizes, and reports accuracy on a held-out test set.

Bind to Hardware

Register UIDs of your certified devices. The binary only executes on authorized hardware.

Integrate & Submit

Link the compiled library into your firmware. Deterministic output supports regulatory submission.

Building a medical device with AI?

Start with the free Evaluation plan or contact us for regulatory-focused support.

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