Finger Motion Detection for Human Activities Recognition using Single sEMG Channel

Yang Qian, Ichiro Yamada, Shin'ichi Warisawa

2014

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 motions’ 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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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