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Reality AI can recognize subtle signatures in acceleration and vibration, even in high variation applications and noisy environments. Typical applications are wearables, and machine health / predictive maintenance.
Typical sample rates when working with accelerometers and vibration sensors
You'll get your best results with Reality AI when you use time waveforms as your input data and you select an appropriate sample rate. If time waveform isn't practical, we can also work with FFT as input - though we prefer as much frequency and time resolution as possible. In some cases our tools can get good results with Peak-to-Peak, RMS and similar values, but the more information the tools can get in the frequency and time domains, the better.
Try Reality AI Starter Kit
Includes Bosch XDK sensor modules
+ 2 months access to Reality AI Tools™
> Collect accelerometer and vibration data
> Detect anomalies, or create labeled classes
> Create detectors for specific events and conditions
> Explore relationship between sample rate, detection window,
computational complexity, and detection accuracy
> Determine hardware requirements for embedded AI solutions
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Learn about some of the technical details behind the Reality AI approach to machine learning with signals