DocumentCode
71846
Title
A Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors
Author
Pyeong-Gook Jung ; Gukchan Lim ; Seonghyok Kim ; Kyoungchul Kong
Author_Institution
Dept. of Mech. Eng., Sogang Univ., Seoul, South Korea
Volume
11
Issue
2
fYear
2015
fDate
Apr-15
Firstpage
485
Lastpage
494
Abstract
Recognition of human gestures plays an important role in a number of human-interactive applications, such as mobile phones, health monitoring systems, and human-assistive robots. Electromyography (EMG) is one of the most common and intuitive methods used for detecting gestures based on muscle activities. The EMG, however, is in general, too sensitive to environmental disturbances, such as electrical noise, electromagnetic signals, humidity, and so on. In this paper, a new method for recognizing the muscular activities is proposed based on air-pressure sensors and air-bladders. The muscular activity is detected by measuring the change of the air pressure in an air-bladder contacting the interested muscle(s). Since the change of the air pressure can be more robustly measured compared with the change of electric signals appeared on the skin, the proposed sensing method is useful for mobile devices due to its great signal-to-noise ratio (SNR) and fast response time. The principle and applications of the proposed sensing method are introduced in this paper. The performance of the proposed method is evaluated in terms of linearity, repeatability, wear-comfort, etc., and is also verified by comparing it with an EMG signal and a motion sensor.
Keywords
electromyography; gesture recognition; medical signal detection; pressure sensors; wearable computers; EMG; SNR; air-pressure sensors; electric signal; electromyography; gesture detection; human-interactive application; motion sensor; muscular activities detection; muscular activity recognition; signal-to-noise ratio; wearable gesture recognition device; Electromyography; Force; Informatics; Mobile handsets; Muscles; Noise; Sensors; Electromyography (EMG); Gesture recognition; Mechanomyography; Wearable sensors; gesture recognition; mechanomyography (MMG); mobile phones; wearable device; wearable sensors;
fLanguage
English
Journal_Title
Industrial Informatics, IEEE Transactions on
Publisher
ieee
ISSN
1551-3203
Type
jour
DOI
10.1109/TII.2015.2405413
Filename
7045532
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