A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation (Hernandez-Rebollar 2002)

Summary:
This paper recognizes static postures from the sign language alphabet from data collected through dual-axis accelerometers mounted on each of the fingers. The ten dimensional data (five fingers with two directions of accelerometer sensing each) collected for each letter is reduced to 3 dimensions. The X-global and Y-global features are extracted from the 10 dimensional data as well as the index finger's height. Plotting distributions of these three features in 3D helps visualize clusters of data. The 3D data are projected onto planes to better decide dividing points for the classifiers. Gestures are identified by a hierarchical three level classifier.
Ten posture readings were collected for each letter from each of from five volunteers. The posture readings for letters 'J' and 'Z' were collected at the end of the motion for the letter. Once the data has been analyzed and boundaries for the classifiers have been set up, they are programmed onto a microcontroller which is connected to a speech synthesizer so that the letters formed by hand postures can be heard. Twenty one of the 26 letters had a recognition rate of 100%. The worst recognition rate was 78% for the letter 'U'.

Discussion:
The statistics reported about the accelerometers' resolution and the diagram describing the hierarchical classifier are sufficiently detailed. One unique aspect of this system is that it uses a computer only for off-line analyzation of data and not during the online recognition. The use of a microcontroller for recognition makes the prototype system portable and closer to a system that could actually be used in real life than most of the other systems covered in the literature.