  In modern society, people should not be identified based on their disability,
rather, it is environments that can disable people with impairments.
Improvements to automatic Sign Language Recognition (SLR) will lead to more
enabling environments via digital technology. Many state-of-the-art approaches
to SLR focus on the classification of static hand gestures, but communication
is a temporal activity, which is reflected by many of the dynamic gestures
present. Given this, temporal information during the delivery of a gesture is
not often considered within SLR. The experiments in this work consider the
problem of SL gesture recognition regarding how dynamic gestures change during
their delivery, and this study aims to explore how single types of features as
well as mixed features affect the classification ability of a machine learning
model. 18 common gestures recorded via a Leap Motion Controller sensor provide
a complex classification problem. Two sets of features are extracted from a 0.6
second time window, statistical descriptors and spatio-temporal attributes.
Features from each set are compared by their ANOVA F-Scores and p-values,
arranged into bins grown by 10 features per step to a limit of the 250
highest-ranked features. Results show that the best statistical model selected
240 features and scored 85.96% accuracy, the best spatio-temporal model
selected 230 features and scored 80.98%, and the best mixed-feature model
selected 240 features from each set leading to a classification accuracy of
86.75%. When all three sets of results are compared (146 individual machine
learning models), the overall distribution shows that the minimum results are
increased when inputs are any number of mixed features compared to any number
of either of the two single sets of features.
