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Active Magnetoelectric Motion Sensing: Examining Performance Metrics with an Experimental Setup.
Johannes Hoffmann1, Eric Elzenheimer1, Christin Bald1
1Institute of Electrical Engineering and Information Technology, Faculty of Engineering, Kiel University, 24143 Kiel, Germany.
Sensors (Basel, Switzerland)
|December 10, 2021
Summary
This study introduces an active magnetic motion sensing system for magnetoelectric (ME) sensors. The system enhances signal-to-noise ratio for potential use in medical movement analysis.
Area of Science:
- Physics
- Sensor Technology
- Biomedical Engineering
Background:
- Magnetoelectric (ME) sensors offer low magnetic noise density (pT/Hz) but have limitations in bandwidth for direct biomagnetic measurements.
- High resonance frequencies (kHz) and limited bandwidth of ME sensors can be leveraged for indirect sensing of non-magnetic quantities using artificial magnetic sources.
Purpose of the Study:
- To present a novel active magnetic motion sensing system optimized for ME sensors.
- To investigate and quantify key drivers of the signal-to-noise ratio (SNR) in the ME sensor signal chain.
- To demonstrate the system's capabilities in a 1D motion setup.
Main Methods:
- Development of an active magnetic motion sensing system tailored for ME sensors.
- Analysis of the signal chain to identify critical factors influencing SNR.
- Implementation of a customized filter structure for flexible bandwidth selection and frequency-based source separation.
Main Results:
- Quantification of SNR drivers, linking sensor noise and bandwidth to overall performance.
- Successful demonstration of the sensing system in a simplified one-dimensional motion experiment.
- Development of a filter structure enabling adaptable bandwidth and multi-source separation.
Conclusions:
- The proposed active magnetic motion sensing system shows promise for indirect sensing applications.
- The developed filter structure facilitates flexible bandwidth control and source separation, crucial for complex environments.
- This technology targets future applications in medical movement analysis utilizing distributed ME sensors and artificial sources.
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