Fall Detection Data Augmentation Flowchart

シナリオ

A team building a wearable fall detection model needs to expand a small labeled sensor dataset. This flowchart shows how raw accelerometer windows are labeled and augmented by rotation and scaling before model training.

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Using 3D rotation to simulate device orientation changes

Using magnitude scaling to simulate varying intensity

Merging fall and non-fall augmented copies into one balanced dataset

Reusable when designing synthetic data pipelines for inertial sensor classification, especially when collecting real fall events is expensive or risky.

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Fall Detection Data Augmentation Pipeline

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