AI Data for the IoMT Ecosystem
Annotate time-series sensor data and wearable biometrics to power predictive health AI. Move healthcare beyond the clinic and into the home.
Industry Overview
The Internet of Medical Things (IoMT) is shifting patient care from hospitals to the home. Dserve AI helps companies train predictive algorithms on continuous, real-time physiological data collected from wearables and remote sensors.
AI Challenges
Sensor data is noisy, continuous, and highly complex. Time-series data from ECGs, sleep monitors, and glucose sensors require precise temporal annotation to train algorithms to detect anomalies before they become critical.
Dserve AI Solutions
We specialize in time-series annotation. Our teams label anomalies, categorize sleep stages, and map physiological events across continuous streams of wearable data, providing the exact timestamps required to train predictive health AI.
High-Impact AI Use Cases
Discover how our specialized data solutions power state-of-the-art models and drive measurable outcomes across real-world domain projects.
Arrhythmia Detection
Annotating continuous ECG and PPG sensor streams to identify irregular heartbeats.
Specialized annotation pipeline designed for enterprise scale and accuracy.
Sleep Stage Classification
Labeling accelerometer and heart rate data to determine REM and deep sleep cycles.
Specialized annotation pipeline designed for enterprise scale and accuracy.
Fall Detection Algorithms
Tagging sudden kinetic shifts in accelerometer data for elderly monitoring systems.
Specialized annotation pipeline designed for enterprise scale and accuracy.
Specialized Expertise
- Time-series sensor data annotation
- ECG & PPG signal labeling
- Wearable accelerometer/gyroscope annotation
- Sleep & activity state classification
- Anomaly & event detection tagging
Why Choose Us
We have proprietary tooling specifically designed for visualizing and annotating continuous time-series data, allowing our teams to tag micro-events with millisecond precision.
How do you handle massive streams of continuous data?
We use specialized time-series annotation tools that allow our annotators to zoom in on micro-events and label precise temporal segments.
Can you synchronize data from multiple sensors?
Yes, we frequently annotate multi-modal data, synchronizing video feeds with wearable sensor output to provide comprehensive context.
What types of biometric data do you process?
We handle everything from ECG and EEG waveforms to simple accelerometer, temperature, and glucose continuous streams.
Ready to Accelerate Your AI?
Talk to our Remote Patient Monitoring data experts and start your custom pilot project today.
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