QinetiQ AAOP2011 Abstract
AN ADVANCED LOWER-LIMB PROTHESIS FOR BATTLEFIELD AMPUTEES Faheem, F.F.1, Edell, D.J.2, and Hanson, W.J.3 1
QinetiQ NA Technology Solutions Group, Waltham MA , 2 3 InnerSea Tech, Bedford MA , Liberating Tech, Holliston MA
INTRODUCTION Modern microprocessor-based prosthetic knees have improved gait and stability, especially for level ground walking. However, current systems are limited in their ability to adapt to changes, because they react only to kinetic and kinematic inputs. This system could improve, further reducing the need for compensatory gait strategies, improving navigation on ramps, stairs and uneven terrain, and approximating more natural reflex control, as for stumble recovery. Our QinetiQ North America TSG team is building a stabler and safer system, with a more predictive rather than reactive prosthesis that features approximate reflexes through physiological pathways, enhancing slip and stumble recovery and responding seamlessly to such changing conditions as stairs, ramps, uneven terrain, etc.
oped an acquisition system with sensors and EMG electrodes, and analyzed data from able-bodied and amputee subjects. We developed GUI software and hardware for a 32-channel backpack data acquisition system, and refined filtering, incorporated the new hardware, and debugged the system. We demonstrated system integration of a proof-of-concept knee.
METHOD
Figure 2. EMG signals indicate muscle activity during gait cycle
DISCUSSION Combining EMG and kinetic and kinematic signals yields more accurate positioning, for a more predictive than reactive prosthesis system. Figure 1. The system consists of integration of electrode, acquiring signal, signal processing, and controlling a mechanical knee.
Figure 1 shows the four major methods we use to validate our strategy of an EMG-controlled knee system approach. Two IRBs / ORBs have been approved to acquire signal from able-bodied and amputee subjects. We identify muscle signal location by an electrode array; obtain high-quality surface biological signals; integrate the biological signals with kinetic and kinematic inputs; and control the microprocessor knee. RESULTS We integrated e-textile in the amputee socket, developed fabric electrodes, and tested performance. Our team developed software and hardware to measure EMG signals for locating optimal recording sites, devel-
37th
CONCLUSION Nothing commercially available today has a microprocessor knee (MPK) with EMG control, as well as energysaving features. This program will enable an MPK with biological feedback and will provide awareness to subjects, giving them better safety and stability performance. ACKNOWLEDGMENTS This research and development project was conducted by the QinetiQ North America TSG team and is made possible by a contract awarded and administered by the U.S. Army Medical Research & Materiel Command (USAMRMC) and the Telemedicine & Advanced Technology Research Center (TATRC) under contract W81XWH08C0729.
American Academy of Orthotists & Prosthetists Academy Annual Meeting and Scientific Symposium March 16-19, 2011
QinetiQ NA Technology Solutions Group, Waltham MA , 2 3 InnerSea Tech, Bedford MA , Liberating Tech, Holliston MA
INTRODUCTION Modern microprocessor-based prosthetic knees have improved gait and stability, especially for level ground walking. However, current systems are limited in their ability to adapt to changes, because they react only to kinetic and kinematic inputs. This system could improve, further reducing the need for compensatory gait strategies, improving navigation on ramps, stairs and uneven terrain, and approximating more natural reflex control, as for stumble recovery. Our QinetiQ North America TSG team is building a stabler and safer system, with a more predictive rather than reactive prosthesis that features approximate reflexes through physiological pathways, enhancing slip and stumble recovery and responding seamlessly to such changing conditions as stairs, ramps, uneven terrain, etc.
oped an acquisition system with sensors and EMG electrodes, and analyzed data from able-bodied and amputee subjects. We developed GUI software and hardware for a 32-channel backpack data acquisition system, and refined filtering, incorporated the new hardware, and debugged the system. We demonstrated system integration of a proof-of-concept knee.
METHOD
Figure 2. EMG signals indicate muscle activity during gait cycle
DISCUSSION Combining EMG and kinetic and kinematic signals yields more accurate positioning, for a more predictive than reactive prosthesis system. Figure 1. The system consists of integration of electrode, acquiring signal, signal processing, and controlling a mechanical knee.
Figure 1 shows the four major methods we use to validate our strategy of an EMG-controlled knee system approach. Two IRBs / ORBs have been approved to acquire signal from able-bodied and amputee subjects. We identify muscle signal location by an electrode array; obtain high-quality surface biological signals; integrate the biological signals with kinetic and kinematic inputs; and control the microprocessor knee. RESULTS We integrated e-textile in the amputee socket, developed fabric electrodes, and tested performance. Our team developed software and hardware to measure EMG signals for locating optimal recording sites, devel-
37th
CONCLUSION Nothing commercially available today has a microprocessor knee (MPK) with EMG control, as well as energysaving features. This program will enable an MPK with biological feedback and will provide awareness to subjects, giving them better safety and stability performance. ACKNOWLEDGMENTS This research and development project was conducted by the QinetiQ North America TSG team and is made possible by a contract awarded and administered by the U.S. Army Medical Research & Materiel Command (USAMRMC) and the Telemedicine & Advanced Technology Research Center (TATRC) under contract W81XWH08C0729.
American Academy of Orthotists & Prosthetists Academy Annual Meeting and Scientific Symposium March 16-19, 2011
