  Online recognition of gestures is critical for intuitive human-robot
interaction (HRI) and further push collaborative robotics into the market,
making robots accessible to more people. The problem is that it is difficult to
achieve accurate gesture recognition in real unstructured environments, often
using distorted and incomplete multisensory data. This paper introduces an HRI
framework to classify large vocabularies of interwoven static gestures (SGs)
and dynamic gestures (DGs) captured with wearable sensors. DG features are
obtained by applying data dimensionality reduction to raw data from sensors
(resampling with cubic interpolation and principal component analysis).
Experimental tests were conducted using the UC2017 hand gesture dataset with
samples from eight different subjects. The classification models show an
accuracy of 95.6% for a library of 24 SGs with a random forest and 99.3% for 10
DGs using artificial neural networks. These results compare equally or
favorably with different commonly used classifiers. Long short-term memory deep
networks achieved similar performance in online frame-by-frame classification
using raw incomplete data, performing better in terms of accuracy than static
models with specially crafted features, but worse in training and inference
time. The recognized gestures are used to teleoperate a robot in a
collaborative process that consists in preparing a breakfast meal.
