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Authors: Marcus Georgi ; Christoph Amma and Tanja Schultz

Affiliation: Karlsruhe Institute of Technology, Germany

ISBN: 978-989-758-069-7

Keyword(s): Wearable Computing, Gesture Recognition, Inertial Measurement Unit, Electromyography.

Related Ontology Subjects/Areas/Topics: Animation and Simulation ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Health Information Systems ; Human-Computer Interaction ; Methodologies and Methods ; Motion Control ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Wearable Sensors and Systems

Abstract: Session- and person-independent recognition of hand and finger gestures is of utmost importance for the practicality of gesture based interfaces. In this paper we evaluate the performance of a wearable gesture recognition system that captures arm, hand, and finger motions by measuring movements of, and muscle activity at the forearm. We fuse the signals of an Inertial Measurement Unit (IMU) worn at the wrist, and the Electromyogram (EMG) of muscles in the forearm to infer hand and finger movements. A set of 12 gestures was defined, motivated by their similarity to actual physical manipulations 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 datacorpus will be 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 rec ognition rate of 97.8% in session-independent, and of 74.3% in person-independent recognition. Additionally, 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 (More)

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Paper citation in several formats:
Georgi M., Amma C. and Schultz T. (2015). Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 99-108. DOI: 10.5220/0005276900990108

@conference{biosignals15,
author={Marcus Georgi and Christoph Amma and Tanja Schultz},
title={Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={99-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005276900990108},
isbn={978-989-758-069-7},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing
SN - 978-989-758-069-7
AU - Georgi M.
AU - Amma C.
AU - Schultz T.
PY - 2015
SP - 99
EP - 108
DO - 10.5220/0005276900990108

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