Enabling fast and effortless customisation in accelerometer based gesture interaction

Enabling fast and effortless customisation in accelerometer based gesture interaction

Mäntyjärvi et al.

Create gestures using accelerometers. First the gesture is resampled to 40 points.(?) Each resampled point is then vector quantized into one of eight codewords. The resampled, vector quantized data is then input to a set of HMMs. To create their training set, rather than force each user to repeat gestures multiple times, the authors added noise to the data samples, testing both uniform and gaussian noise distributions. The entire set was used to determine the vector quantization codebook, but was divided for HMM training and testing. The gestures used represent DVD player instructions and are 2D symbols. The system was tested using varios numbers of training examples for the HMMs, various amounts of noise, and various amounts of noisy data.


Discussion
Gesture set is just 2D, could use any sketch recognition system for as good or better results, no need to train HMMs. Gesture set is also very simple which is the only reason they can get away with using only 8 codewords and train HMMs on tiny datasets (1-2 examples).

Reference
Enabling fast and effortless customisation in accelerometer based gesture interaction Mantyjarvi, J., Kela, J., Korpipaa, P. and Kallio S. Proc. of the MUM '04, ACM Press (2004)