  Gestures are a natural communication modality for humans. The ability to
interpret gestures is fundamental for robots aiming to naturally interact with
humans. Wearable sensors are promising to monitor human activity, in particular
the usage of triaxial accelerometers for gesture recognition have been
explored. Despite this, the state of the art presents lack of systems for
reliable online gesture recognition using accelerometer data. The article
proposes SLOTH, an architecture for online gesture recognition, based on a
wearable triaxial accelerometer, a Recurrent Neural Network (RNN) probabilistic
classifier and a procedure for continuous gesture detection, relying on
modelling gesture probabilities, that guarantees (i) good recognition results
in terms of precision and recall, (ii) immediate system reactivity.
