Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors
This addresses the lack of systems for reliable online gesture recognition for robots aiming to naturally interact with humans, but it appears incremental as it builds on existing methods like RNNs and accelerometers.
The authors tackled the problem of reliable online gesture recognition using accelerometer data by proposing SLOTH, an architecture based on a wearable triaxial accelerometer and a Recurrent Neural Network, which achieved good recognition results in terms of precision and recall and immediate system reactivity.
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.