TY - JOUR
T1 - Decoding of individuated finger movements using surface electromyography
AU - Tenore, Francesco V.G.
AU - Ramos, Ander
AU - Fahmy, Amir
AU - Acharya, Soumyadipta
AU - Etienne-Cummings, Ralph
AU - Thakor, Nitish V.
N1 - Funding Information:
Manuscript received January 31, 2008; revised July 10, 2008. First published October 10, 2008; current version published May 22, 2009. This work was supported by the Johns Hopkins University Applied Physics Laboratory under the Defense Advanced Research Projects Agency (DARPA) Revolutionizing Prosthetics Program, Contract N66001-06-C-8005, and by the Fatronik Foundation. Asterisk indicates corresponding author.
PY - 2009/5
Y1 - 2009/5
N2 - Upper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals.While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movementswould require more elaborate control schemes.We showthat it is possible to decode individual flexion and extension movements of each finger (tenmovements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference (p<0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.
AB - Upper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals.While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movementswould require more elaborate control schemes.We showthat it is possible to decode individual flexion and extension movements of each finger (tenmovements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference (p<0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.
KW - Electromyography (EMG)
KW - Myoelectric signals
KW - Neural networks
KW - Transradial amputee
UR - http://www.scopus.com/inward/record.url?scp=67649182962&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67649182962&partnerID=8YFLogxK
U2 - 10.1109/TBME.2008.2005485
DO - 10.1109/TBME.2008.2005485
M3 - Article
C2 - 19473933
AN - SCOPUS:67649182962
SN - 0018-9294
VL - 56
SP - 1427
EP - 1434
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 5
M1 - 4648401
ER -