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Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data

Koch, Philipp, Brügge, Nele, Phan, Huy, Maass, Marco, Mertins, Afred (2019) Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data. In: 44th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019). . IEEE ISBN 978-1-4799-8131-1. (doi:10.1109/ICASSP.2019.8682986)

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For many applications of hand gesture recognition, a delayfree, 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.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/ICASSP.2019.8682986
Uncontrolled keywords: Inertial measurement unit, hand gesture recognition, recurrent neural network, multi-modal fusion
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Huy Phan
Date Deposited: 20 Feb 2019 17:27 UTC
Last Modified: 15 Jan 2020 12:07 UTC
Resource URI: (The current URI for this page, for reference purposes)
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