Towards the control of individual fingers of a prosthetic hand using surface EMG signals.
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.
Tenore
Francesco
F
Department of Electrical & Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA.
Ramos
Ander
A
Fahmy
Amir
A
Acharya
Soumyadipta
S
Etienne-Cummings
Ralph
R
Thakor
Nitish V
NV
D000200:Action Potentials / Q000502:physiology; D000674:Amputees / Q000534:rehabilitation; D001185:Artificial Intelligence; D001186:Artificial Limbs; D001696:Biomechanical Phenomena / Q000295:instrumentation; D011211:Electric Power Supplies; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005246:Feedback; D005385:Fingers; D006225:Hand; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011474:Prosthesis Design; D012371:Robotics / Q000295:instrumentation*; D013813:Therapy, Computer-Assisted / Q000379:methods