We held our next tinyML Talks webcast. Jenny Plunkett from Edge Impulse presented Advanced Anomaly Detection Made Easy on February 15, 2022.
Being able to detect anomalies is becoming an extremely useful technique in the world of embedded ML, and one that can be done on the most constrained always-on devices. Anomaly detection can be used for a multitude of use cases, from cold chain monitoring to fault detection on industrial machinery or satellites.
In this talk we will present how to use data-driven engineering to create your data set and use Edge Impulse to create a model able to classify anomalous sensor readings. We will do this by leveraging some new powerful features in Edge Impulse.
You will learn to:
· Implement and use custom DSP blocks to analyze your IoT data and extract the most important features
· harness the value of feature importance to zoom in on interesting frequency bands
· iterate over thresholds in anomaly detection blocks, in order to find the optimal configuration
Jenny Plunkett is a Texas Longhorn and software engineer, working as a Senior User Success Engineer at Edge Impulse. Since graduating from The University of Texas she has been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for the Pelion device management platform.
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Watch on YouTube:
Jenny Plunkett
Download presentation slides:
Jenny Plunkett
Feel free to ask your questions on this thread and keep the conversation going!