Sensor Machine Learning

Embedded AI - Intelligence at the Deep Edge

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This podcast explores the rise of Sensor Machine Learning (Sensor ML) as a powerful evolution in embedded system design. Traditional sensor fusion methods like Kalman filters often fall short when faced with non-linear, noisy, or dynamic data. Sensor ML offers a modern alternative by applying machine learning algorithms directly to sensor streams, enabling more accurate pattern recognition, decision-making, and context awareness.

Through real-world examples in autonomous vehicles, wearable tech, predictive maintenance, environmental sensing, and gesture control, the post demonstrates how Sensor ML enhances performance across a wide range of applications. It also addresses the key challenge of deploying these models on constrained devices—an area known as TinyML—emphasizing the importance of model optimization, efficient hardware, and software co-design to deliver intelligent capabilities at the edge.

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