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1.
Article in English | MEDLINE | ID: mdl-19963552

ABSTRACT

This study explored the feasibility of building robust surface electromyography (EMG)-based gesture interfaces starting from the definition of input command gestures. As a first step, an offline experimental scheme was carried out for extracting user-independent input command sets with high class separability, reliability and low individual variations from 23 classes of hand gestures. Then three types (same-user, multi-user and cross-user test) of online experiments were conducted to demonstrate the feasibility of building robust surface EMG-based interfaces with the hand gesture sets recommended by the offline experiments. The research results reported in this paper are useful for the development and popularization of surface EMG-based gesture interaction technology.


Subject(s)
Electromyography/instrumentation , Electromyography/methods , Gestures , Hand/physiology , Pattern Recognition, Automated , Adult , Algorithms , Artificial Intelligence , Electrodes , Female , Humans , Male , Signal Processing, Computer-Assisted , Software , Surface Properties , User-Computer Interface
2.
Article in English | MEDLINE | ID: mdl-19964190

ABSTRACT

This paper investigates the feasibility of building muscle-computer interfaces starting from surface Electromyography (SEMG) -based neck and shoulder motion recognition. In order to reach the research goal, a real-time SEMG sensing, processing and classification system was developed firstly. Then two types of SEMG recognition experiments, namely user-specific and user-independent classification, were designed and conducted on seven kinds of neck and shoulder motions to explore the feasibility of using these motions as input commands of muscle-computer interfaces. In all 9 subjects took part in these experiments, 97.8% and 84.6% overall average recognition accuracies were obtained in user-specific and user-independent experiments respectively. The experimental results demonstrate that it is possible to build muscle-computer interfaces with neck and shoulder motions. In addition, the results of cross-time experiments designed to explore the relationship between training and accuracy in user-specific recognition indicate that users can interact accurately with computers using the defined motions only after four times training in different days.


Subject(s)
Man-Machine Systems , Muscle, Skeletal/physiology , User-Computer Interface , Adult , Biomedical Engineering , Electromyography/statistics & numerical data , Humans , Infant , Male , Movement/physiology , Neck Muscles/physiology , Shoulder , Signal Processing, Computer-Assisted , Young Adult
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