Device control using gestures sensed from EMG

Author

Wheeler, Kevin R.

Author_Institution

NASA Ames Res. Center, Moffett Field, CA, USA

fYear

2003

fDate

23-25 June 2003

Firstpage

21

Lastpage

26

Abstract

In this paper, we present neuro-electric interfaces for virtual device control. The examples presented rely upon sampling electromyogram data from a participant´s forearm. This data is then set into pattern recognition software that had been trained to distinguish gestures from a given gesture set. The pattern recognition software consists of hidden Markov models, which are used to recognize the gestures as they are being performed in real-time. two experiments were conducted to examine the feasibility of this interface technology. The first replicate a virtual joystick interface and the second replicated a keyboard.

Keywords

electromyography; gesture recognition; hidden Markov models; interactive devices; neurocontrollers; pattern recognition; virtual reality; EMG; device control; electromyogram data; gesture distinguishing; gesture recognition; gesture set; gestures sensing; hidden Markov model; interface technology; keyboard replication; neuroelectric interface; participant forearm; pattern recognition software; signal processing; virtual device; virtual joystick interface replication; Computer displays; Computer interfaces; Electrodes; Electromyography; Hidden Markov models; Instruments; Keyboards; Orbital robotics; Pattern recognition; Space missions;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing in Industrial Applications, 2003. SMCia/03. Proceedings of the 2003 IEEE International Workshop on

Print_ISBN

0-7803-7855-5

Type

conf

DOI

10.1109/SMCIA.2003.1231338

Filename

1231338