GestureRecognition with Accelerometers for GameControllers, Phones and Wearables

Anthony D. Whitehead .


Hidden Markov Models have been effectively used in time series based pattern recognition problems in the past. This work explores using Hidden Markov Models (HMM) to do 3D gesture recognition from accelerometer data. Our work dif-fers from much of the previous work in that we examine the use of discreet HMMs rather than continuous HMMs. An interesting side effect of this is that method is therefore theoretically trans-portable to other devices that have a 3D sensor output system. In essence this brings us a mechanism to use the HMM model across a series of different sensor devices for gesture recognition. We achieve recognition results with accuracy rates approaching 90 percent for users who are not in the training samples. The speed of our system is also of interest as we are able to classify gestures at a rate of several hundred times per second. As long as the sen-sor system is capable of outputting information about the 3 axes of motion, and the outputs can be discretized to volumetrically equivalent cubic sub-spaces; that information can then be used in this generic model for accurate, high speed gesture recognition.

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