Authors:
Yang Qian
;
Ichiro Yamada
and
Shin'ichi Warisawa
Affiliation:
The University of Tokyo, Japan
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 mot
ions’ 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.
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