In this project, I trained several Hidden Markov Models (HMMs) to identify different arm motion gestures in real time. The data used to train the models were sensor readings from the accelerometer and the gyroscope of an Inertial Measurement Unit (IMU). These readings corresponded to six different motions: Wave, Infinity, Eight, Circle, Beat3, Beat4.
An HMM was trained for each of these motions. Once trained, given a set of new IMU measurements, the model that assigns the highest probability to the sequence of observations is selected as the motion that the measurements describe. I wrote the code to train and test the HMMs from scratch without the use of HMMs libraries – what a journey!
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