Hand Gesture Sequence Recognition Using Inertial Motion Units (IMUs)

Unlike approaches that classify single gesture at a time, we propose a deep learning based technique that can classify multiple gestures in one shot. This is specially suitable for applications that involves seamless gesture sequences such as sign language recognition, touch-less car assistance systems and gaming systems. We propose a Long Short Term Memory(LSTM) based deep network on the lines of an Encoder-Decoder architecture that classifies gesture sequence accurately in one go. We also show an empirical training strategy for our architecture which can achieve good results even with limited amount of collected data. Results from the experiments performed on labelled datasets from Inertial Motion Units (IMU) proves the efficiency and usefulness of the proposed method.