The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation--with seamless transitions between walking on level ground, stairs, and ramps--and of the ability to reposition the leg while the patient was seated.
Levi J Hargrove, Ann M Simon, Aaron J Young, Robert D Lipschutz, Suzanne B Finucane, Douglas G Smith, and Todd A Kuiken.
Center for Bionic Medicine, Rehabilitation Institute of Chicago, and the Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, Illinois 60611, USA. l-hargrove@northwestern.e... more du less
N. Engl. J. Med.. 2013 Sep 26;369(13):1237-42.
AbstractThe clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patient's intended movements. This provided robust and intuitive control of ambulation--with seamless transitions between walking on level ground, stairs, and ramps--and of the ability to reposition the leg while the patient was seated.