TY - GEN
T1 - Towards the control of individual fingers of a prosthetic hand using surface EMG signals
AU - Tenore, Francesco
AU - Ramos, Ander
AU - Fahmy, Amir
AU - Acharya, Soumyadipta
AU - Etienne-Cummings, Ralph
AU - Thakor, Nitish V.
PY - 2007
Y1 - 2007
N2 - The fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls. Traditional control schemes are only capable of providing 2 degrees of freedom, which is insufficient for dexterous control of individual fingers. We present a framework where myoelectric signals from natural hand and finger movements can be decoded with a high accuracy. 32 surface-EMG electrodes were placed on the forearm of an able-bodied subject while performing individual finger movements. Using time-domain feature extraction methods as inputs to a neural network classifier, we show that 12 individuated flexion and extension movements of the fingers can be decoded with an accuracy higher than 98%. To our knowledge, this is the first instance in which such movements have been successfully decoded using surface-EMG. These preliminary findings provide a framework that will allow the results to be extended to non-invasive control of the next generation of upper-limb prostheses for amputees.
AB - The fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls. Traditional control schemes are only capable of providing 2 degrees of freedom, which is insufficient for dexterous control of individual fingers. We present a framework where myoelectric signals from natural hand and finger movements can be decoded with a high accuracy. 32 surface-EMG electrodes were placed on the forearm of an able-bodied subject while performing individual finger movements. Using time-domain feature extraction methods as inputs to a neural network classifier, we show that 12 individuated flexion and extension movements of the fingers can be decoded with an accuracy higher than 98%. To our knowledge, this is the first instance in which such movements have been successfully decoded using surface-EMG. These preliminary findings provide a framework that will allow the results to be extended to non-invasive control of the next generation of upper-limb prostheses for amputees.
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U2 - 10.1109/IEMBS.2007.4353752
DO - 10.1109/IEMBS.2007.4353752
M3 - Conference contribution
AN - SCOPUS:57649174722
SN - 1424407885
SN - 9781424407880
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 6145
EP - 6148
BT - 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
T2 - 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Y2 - 23 August 2007 through 26 August 2007
ER -