title: Development and quantitative performance evaluation of a noninvasive EMG computer interface.
abstract: This paper describes a noninvasive electromyography (EMG) signal-based computer interface and a performance evaluation method based on Fitts' law. The EMG signals induced by volitional wrist movements were acquired from four sites in the lower arm to extract users' intentions, and six classes of wrist movements were distinguished using an artificial neural network. Using the developed interface, a user can move the cursor, click buttons, and type text on a computer. The test setup was built to evaluate the developed interface, and the mouse was tested by five volunteers with intact limbs. The performance of the developed computer interface and the mouse was tested at 1.299 and 7.733 b/s, respectively, and these results were compared with the performance of a commercial noninvasive brain signal interface (0.386 b/s). The results show that the developed interface performed better than the commercial interface, but less satisfactorily than a computer mouse. Although some issues remain to be resolved, the developed EMG interface has the potential to help people with motor disabilities to access computers and Internet environments in a natural and intuitive manner.
authors.affiliation: Mechanical Engineering Department, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Korea. axlguitar@kaist.ac.kr
authors.forename: Changmok
authors.initials: C
authors.lastname: Choi
authors.forename: Silvestro
authors.initials: S
authors.lastname: Micera
authors.forename: Jacopo
authors.initials: J
authors.lastname: Carpaneto
authors.forename: Jung
authors.initials: J
authors.lastname: Kim
mesh_terms: D003143:Communication Devices for People with Disabilities; D006233:Persons with Disabilities / Q000534:rehabilitation; D004576:Electromyography / Q000379:methods; D005246:Feedback; D005542:Forearm / Q000502:physiology; D006801:Humans; D008328:Man-Machine Systems; D018482:Muscle, Skeletal / Q000502:physiology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013119:Spinal Cord Injuries / Q000534:rehabilitation; D014584:User-Computer Interface