FORKED RECURRENT NEURAL NETWORK FOR HAND GESTURE CLASSIFICATION USING INERTIAL MEASUREMENT DATA

For many applications of hand gesture recognition, a delay-free, affordable, and mobile system relying on body signals is mandatory. Therefore, we propose an approach for hand gestures classification given signals of inertial measurement units (IMUs) that works with extremely short windows to avoid delays. With a simple recurrent neural network the suitability of the sensor modalities of an IMU (accelerometer, gyroscope, magnetometer) are evaluated by only providing data of one modality. For the multi-modal data a second network with mid-level fusion is proposed. Its forked architecture allows us to process data of each modality individually before carrying out a joint analysis for classification. Experiments on three databases reveal that even when relying on a single modality our proposed system outperforms state-of-the-art systems significantly. With the forked network classification accuracy can be further improved by over 10% absolute compared to the best reported system while causing a fraction of the delay.