Case Studyscikit learnactivity recognitionrt threadfirebase
IoT Device Enables Real-Time Activity Classification
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7.0
A full-stack IoT project builds a real-time activity-classification pipeline using an RT-Thread RT-Spark board with an ICM20608 IMU to stream 6-axis motion data over Wi‑Fi to a Flask back-end. The Flask server hosts a serialized scikit-learn model that ingests 30-sample windows, outputs Walking/Running/Stationary predictions with confidence, and writes results to Firebase with a 10‑second throttled history. An Android app displays real-time dashboard and logs.
Scoring Rationale
Practical, end-to-end IoT activity-recognition demo with deployable components; limited novelty and single-source project documentation constrains broader impact.
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