期刊
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
卷 44, 期 2, 页码 293-299出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/THMS.2014.2302794
关键词
Accelerometer; electromyograghy; gesture recognition; human-computer interaction
资金
- Fundamental Research Funds for the Central Universities of China [WK2100230002]
- National Nature Science Foundation of China [61271138]
- Scientific Research Fund of Sichuan Provincial Education Department [12ZA185]
An algorithmic framework is proposed to process acceleration and surface electromyographic (SEMG) signals for gesture recognition. It includes a novel segmentation scheme, a score-based sensor fusion scheme, and two new features. A Bayes linear classifier and an improved dynamic time-warping algorithm are utilized in the framework. In addition, a prototype system, including a wearable gesture sensing device (embedded with a three-axis accelerometer and four SEMG sensors) and an application program with the proposed algorithmic framework for a mobile phone, is developed to realize gesture-based real-time interaction. With the device worn on the forearm, the user is able to manipulate a mobile phone using 19 predefined gestures or even personalized ones. Results suggest that the developed prototype responded to each gesture instruction within 300 ms on the mobile phone, with the average accuracy of 95.0% in user-dependent testing and 89.6% in user-independent testing. Such performance during the interaction testing, along with positive user experience questionnaire feedback, demonstrates the utility of the framework.
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