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Authors: Yang Qian ; Ichiro Yamada and Shin'ichi Warisawa

Affiliation: The University of Tokyo, Japan

ISBN: 978-989-758-010-9

Keyword(s): sEMG, Single sEMG Channel, Finger Motion Detection, Human Activities Recognition.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Biomedical Signal Processing ; Devices ; Health Information Systems ; Human-Computer Interaction ; Pattern Recognition and Machine Learning ; Physiological Computing Systems ; Sensors-Based Applications ; Wearable Sensors and Systems

Abstract: Today’s aging population has recently become a significant problem, requiring a wearable health monitoring system for the elderly who are living alone. One of the focuses of this monitoring system is human activities recognition. We propose a wearable sensing method that is based on muscle’s crosstalk information that uses only one sEMG channel (a pair of electrodes) to recognize five basic finger motions (thumb flexion, index flexion, middle flexion, ring & little flexion, and rest position) related to daily human activities. In the first step, an inter-electrode distance (IED) experiment was conducted to define the suitable IED for crosstalk information collection. In this experiment’s recognition part, a conventional feature extraction method was adopted. The accuracy of each IED was compared and a suitable IED was defined (50 mm). In the second step, we propose two new features, the summit foot range (SFR) and summits number (SN), to represent the different patterns of finger moti ons’ sEMG signals and adopted the minimal Redundancy Maximal Relevance (mRMR) feature selection method to improve the accuracy. An accuracy of over 87% was achieved using the improved recognition methodology compared to 81.5% when using the conventional one. (More)

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Paper citation in several formats:
Qian Y., Yamada I. and Warisawa S. (2014). Finger Motion Detection for Human Activities Recognition using Single sEMG Channel.In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014) ISBN 978-989-758-010-9, pages 60-67. DOI: 10.5220/0004764700600067

@conference{healthinf14,
author={Yang Qian and Ichiro Yamada and Shin'ichi Warisawa},
title={Finger Motion Detection for Human Activities Recognition using Single sEMG Channel},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)},
year={2014},
pages={60-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004764700600067},
isbn={978-989-758-010-9},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)
TI - Finger Motion Detection for Human Activities Recognition using Single sEMG Channel
SN - 978-989-758-010-9
AU - Qian Y.
AU - Yamada I.
AU - Warisawa S.
PY - 2014
SP - 60
EP - 67
DO - 10.5220/0004764700600067

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