Sign Language Recognition Using Sub-units

This chapter discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%

Colorectal Cancer Video for the Deaf Community: A Randomized Control Trial

The Deaf community experiences multiple barriers to accessing cancer information. Deaf participants (n = 144) were randomly assigned to view a colorectal cancer education video or another program in American Sign Language. They completed surveys pre- and post-intervention and at 2 months post-intervention. By using a crossover model, control group participants were offered the option of seeing the intervention video. The experimental group gained and retained significantly more colorectal cancer knowledge than the control group, and the control group demonstrated the greatest knowledge gain after crossing into the experimental arm. This video effectively informed the Deaf community about colorectal cancer