Pilot Study on Prediction of Human Hand Configuration Using Transient State of Surface-Electromyography Signals
- Pilot Study on Prediction of Human Hand Configuration Using Transient State of Surface-Electromyography Signals
- 김민규; 김기훈
- sEMG signals; transient state; pattern recognition
- Issue Date
- 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013)
- Surface electromyography (sEMG) signals have been applied as control commands in numerous humanrobot interface systems and have been deployed for rehabilitation or clinical applications. Although lots of previous workers have tried to determine features appropriate for specific sEMG-signal classification problems, little of this work has involved deeply searching for the inner characteristics of the signals. In this study, we try to evaluate the properties of the transient state of sEMG signals on randomly mounted, dry-type electrodes and use this to rapidly predict three kinds of hand configurations - rock, scissors and paper motions. In experiments, subjects performed a rock-scissor-paper game with a virtual hand. For data acquisition, the sEMG signals were sampled at 1 kHz with eightchannel electrodes (wearable, dry type) that were randomly mounted on forearms . The results verified that the proposed algorithm, using the property of the transient state of sEMG signals, works successfully.
- Appears in Collections:
- KIST Publication > Conference Paper
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.