Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Entertainment feature of a game using skin conductance response
Proceedings of the 2004 ACM SIGCHI International Conference on Advances in computer entertainment technology
Intimate interfaces in action: assessing the usability and subtlety of emg-based motionless gestures
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hand gestures for HCI using ICA of EMG
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
Real-time classification of electromyographic signals for robotic control
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors
Proceedings of the 14th international conference on Intelligent user interfaces
Making muscle-computer interfaces more practical
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Study of myoelectric prosthese based on improved LS-SVM and fuzzy control
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
Design of a low-cost five-finger anthropomorphic robotic arm with nine degrees of freedom
Robotics and Computer-Integrated Manufacturing
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part I
Emerging Input Technologies for Always-Available Mobile Interaction
Foundations and Trends in Human-Computer Interaction
Beauty technology: muscle based computing interaction
Proceedings of the 2013 ACM international conference on Interactive tabletops and surfaces
| Hi-index | 0.02 |
In this paper the development of an electromyogram (EMG) based interface for hand gesture recognition is presented. To recognize control signs in the gestures, we used a single channel EMG sensor positioned on the inside of the forearm. In addition to common statistical features such as variance, mean value, and standard deviation, we also calculated features from the time and frequency domain including Fourier variance, region length, zerocrosses, occurrences, etc. For realizing real-time classification assuring acceptable recognition accuracy, we combined two simple linear classifiers (k-NN and Bayes) in decision level fusion. Overall, a recognition accuracy of 94% was achieved by using the combined classifier with a selected feature set. The performance of the interfacing system was evaluated through 40 test sessions with 30 subjects using an RC Car. Instead of using a remote control unit, the car was controlled by four different gestures performed with one hand. In addition, we conducted a study to investigate the controllability and ease of use of the interface and the employed gestures.