TY - GEN
T1 - Surface EMG and intra-socket force measurement to control a prosthetic device
AU - Sanford, Joe
AU - Patterson, Rita
AU - Popa, Dan
N1 - Publisher Copyright:
© 2015 SPIE.
PY - 2015
Y1 - 2015
N2 - Surface electromyography (SEMG) has been shown to be a robust and reliable interaction method allowing for basic control of powered prosthetic devices. Research has shown a marked decrease in EMG-classification efficiency throughout activities of daily life due to socket shift and movement and fatigue as well as changes in degree of fit of the socket throughout the subject's lifetime. Users with the most severe levels of amputation require the most complex devices with the greatest number of degrees of freedom. Controlling complex dexterous devices with limited available inputs requires the addition of sensing and interaction modalities. However, the larger the amputation severity, the fewer viable SEMG sites are available as control inputs. Previous work reported the use of intra-socket pressure, as measured during wrist flexion and extension, and has shown that it is possible to control a powered prosthetic device with pressure sensors. In this paper, we present data correlations of SEMG data with intra-socket pressure data. Surface EMG sensors and force sensors were housed within a simulated prosthetic cuff fit to a healthy-limbed subject. EMG and intra-socket force data was collected from inside the cuff as a subject performed pre-defined grip motions with their dominant hand. Data fusion algorithms were explored and allowed a subject to use both intra-socket pressure and SEMG data as control inputs for a powered prosthetic device. This additional input modality allows for an improvement in input classification as well as information regarding socket fit through out activities of daily life.
AB - Surface electromyography (SEMG) has been shown to be a robust and reliable interaction method allowing for basic control of powered prosthetic devices. Research has shown a marked decrease in EMG-classification efficiency throughout activities of daily life due to socket shift and movement and fatigue as well as changes in degree of fit of the socket throughout the subject's lifetime. Users with the most severe levels of amputation require the most complex devices with the greatest number of degrees of freedom. Controlling complex dexterous devices with limited available inputs requires the addition of sensing and interaction modalities. However, the larger the amputation severity, the fewer viable SEMG sites are available as control inputs. Previous work reported the use of intra-socket pressure, as measured during wrist flexion and extension, and has shown that it is possible to control a powered prosthetic device with pressure sensors. In this paper, we present data correlations of SEMG data with intra-socket pressure data. Surface EMG sensors and force sensors were housed within a simulated prosthetic cuff fit to a healthy-limbed subject. EMG and intra-socket force data was collected from inside the cuff as a subject performed pre-defined grip motions with their dominant hand. Data fusion algorithms were explored and allowed a subject to use both intra-socket pressure and SEMG data as control inputs for a powered prosthetic device. This additional input modality allows for an improvement in input classification as well as information regarding socket fit through out activities of daily life.
KW - Assistive and Rehabilitation Robotics
KW - Hand Biomechanics
KW - Human Machine Interface
KW - Physical Human Robot Interaction
KW - Pressure Sensitive Robot Skin
UR - http://www.scopus.com/inward/record.url?scp=84938717631&partnerID=8YFLogxK
U2 - 10.1117/12.2177399
DO - 10.1117/12.2177399
M3 - Conference contribution
AN - SCOPUS:84938717631
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation
A2 - Blowers, Misty
A2 - Wijesundara, Muthu B. J.
A2 - Popa, Dan
PB - SPIE
Y2 - 21 April 2015 through 22 April 2015
ER -