Enabling Fast and Effortless Customisation in Accelerometer Based Gesture Interaction

This paper presents use of the acceleration data for the gesture recognition using the HMM's. The gestures used are used for controlling the DVD player controls.They are using the accelerometers to capture the acceleration values and as per them these signal patterns can be used in generating models that allow the recognition of gestures using an HMM. The process involves:

1. Using the sensors to obtain the accelerometer data.
2. Sampling the data again and normalizing the same equal to equal length and amplitude. The data is reduced to 40 sample points per gesture.
3. Sending the data for a gesture to the Vector Quantizer to reduce the dimensionality of the data to 1-D. (Assigning labels)
4. Then the vector quantized data is used to train the HMM which is then used for the recognition.

For their training, they are adding artificial noise to introduce variability and thus increasing their training data set. They found that with SNR=3 Gaussian noise, data achieved best accuracy. They collected 30 distinct 3D acceleration vectors from one person and selected 8 gestures for the control. Then they added noise to introduce variability and get additional data. They then used the data for cross validation to obtain the best training/ testing set . They got an accuracy of 97.2% with SNR=3.


Discussion:

This paper was strange for me. I don't understand, If I am correct they are using the same data for vector quantization (after adding nose too) and then they are testing on part of the data. And after using vector quantization why are they using HMM's. Also addition of noise cannot introduce the variability that different user can introduce, so their accuracy means nothing to me.