Real-Time Classification of Forearm Electromyographic Signals Corresponding to User-Selected Intentional Movements for Multifunction Prosthesis Control

@article{Momen2007RealTimeCO,
  title={Real-Time Classification of Forearm Electromyographic Signals Corresponding to User-Selected Intentional Movements for Multifunction Prosthesis Control},
  author={K. Momen and Sridhar Krishnan and Tom Chau},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  year={2007},
  volume={15},
  pages={535-542}
}
Pattern recognition-based multifunction prosthesis control strategies have largely been demonstrated with subsets of typical able-bodied hand movements. These movements are often unnatural to the amputee, necessitating significant user training and do not maximally exploit the potential of residual muscle activity. This paper presents a real-time electromyography (EMG) classifier of user-selected intentional movements rather than an imposed subset of standard movements. EMG signals were… CONTINUE READING
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