Use Cases
- Real-time compression of multi-sensor biomedical streams
- Low memory usage suitable for battery-powered devices
- 20-50 MB/s throughput on embedded cores
- High-fidelity signal preservation for research
- Compatibility with multi-modal datasets
- Reduced storage footprint for long-term studies
- Significantly faster cloud and edge transmission
- High-frequency embedded sensor device support
- Clinical integrity maintained throughout
Features
Purpose-Built for Medical Data: Optimized around the structure and temporal patterns of physiological signals, delivering significantly better compression ratios than generic algorithms
Lossless & Clinically Safe: Every sample, spike, and waveform is preserved exactly, ensuring signal quality suitable for research, clinical use, and regulatory-sensitive workflows
Designed for Real-World Deployment: Integrates effortlessly into cloud pipelines, edge gateways, and high-frequency embedded sensor devices
High Throughput on Embedded Cores: 20-50 MB/s throughput with real-time compression of multi-sensor biomedical streams and low memory usage suitable for battery-powered devices
Embedded-Friendly Performance: Engineered to operate efficiently on ESP32-class devices, ARM Cortex-M series (Cortex-M0/M3/M4/M7), and low-power wearable and IoT platforms
Benefits
Deployment Options
Deploy on ESP32-class devices for real-time biomedical signal compression with minimal power consumption.
Run on ARM Cortex-M series (Cortex-M0/M3/M4/M7) for low-power wearable and IoT platforms.
Integrate into cloud pipelines and edge gateways for high-frequency embedded sensor device support.