AI-based analysis of biosignals from wearables
Particularly in the recording of biosignals from wearables, latent motion artifacts or noise often make reliable data analysis difficult. The risk of error is exacerbated by a lack of computing capacity sufficient for recording and processing the vast quantities of data.
We at Fraunhofer IIS develop AI-based algorithms for analyzing biosignals from mobile measurement systems. These can be implemented in an energy-efficient way on various hardware platforms and allow for reliable data interpretation, even when motion artifacts are present.
By optimizing neural networks, we can provide near-sensor AI implementation with real‑time data evaluation.
Examples of available algorithms
- Analysis of respiratory parameters
- Sensor data fusion
- Analysis and reconstruction of human motion (e.g. walking, running, cycling)
- Detection of heart rate and heart rate variability
- Arrhythmia detection (e.g. atrial fibrillation)
If you want to get more than just random noise out of your data set, give us a call – we would be happy to find the right solution for you!
We license our existing algorithms or develop individual analysis solutions tailored to our customers’ requirements.