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2015 | OriginalPaper | Chapter

Fusion and Comparison of IMU and EMG Signals for Wearable Gesture Recognition

Authors : Marcus Georgi, Christoph Amma, Tanja Schultz

Published in: Biomedical Engineering Systems and Technologies

Publisher: Springer International Publishing

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Abstract

We evaluate the performance of a wearable gesture recognition system for arm, hand, and finger motions, using the signals of an Inertial Measurement Unit (IMU) worn at the wrist, and the Electromyogram (EMG) of muscles in the forearm. A set of 12 gestures was defined, similar to manipulatory movements and to gestures known from the interaction with mobile devices. We recorded performances of our gesture set by five subjects in multiple sessions. The resulting data corpus is made publicly available to build a common ground for future evaluations and benchmarks. Hidden Markov Models (HMMs) are used as classifiers to discriminate between the defined gesture classes. We achieve a recognition rate of 97.8 % in session-independent, and of 74.3 % in person-independent recognition. We give a detailed analysis of error characteristics and of the influence of each modality to the results to underline the benefits of using both modalities together.

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previous chapter A Novel Application of Universal Background Models for Periocular Recognition

next chapter Integrating User-Centred Design in the Development of a Silent Speech Interface Based on Permanent Magnetic Articulography

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Title: Fusion and Comparison of IMU and EMG Signals for Wearable Gesture Recognition
Authors: Marcus Georgi
Christoph Amma
Tanja Schultz
Publisher: Springer International Publishing
Book: Biomedical Engineering Systems and Technologies
Print ISBN: 978-3-319-27706-6

Electronic ISBN: 978-3-319-27707-3

Copyright Year: 2015
DOI: https://doi.org/10.1007/978-3-319-27707-3_19

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