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Motion Commotion: Using Hidden Markov Models in Gesture Recognition

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The challenge of classifying large and confusing amounts of data is an ever-present challenge in today’s society, as cheap networks and sensors become increasingly available. Hidden Markov Models (HMM’s) represent an easy to understand, yet provably powerful way of modelling time-varying processes. In my presentation, I will introduce the concept of the HMM , outline a number of associated algorithms, and explain the role of HMM ’s in gesture recognition – particularly in my Part II project. Finally, I will apologise for the terrible catchphrase in the title.

This talk is part of the Churchill CompSci Talks series.

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