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

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #72659 Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data
sameAs	Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data
http://eprints.org/ontology/hasAccepted	Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data (PDF)
http://eprints.org/ontology/hasDocument	Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data (PDF) Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data (Other) Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data (Other) Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data (Other) Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data (Other) Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data (Other)
dc:hasVersion	Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data (PDF)
Title	Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-04-17
Creator	Nele Brügge Huy Phan Philipp Koch Marco Maass Afred Mertins
status	peer reviewed published
Publisher	IEEE
abstract	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.
Is Part Of	https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/72659#authors
presented at	44th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #72659 Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement Data

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