Robotic leg control with EMG decoding in an amputee with nerve transfers

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 patternrecognition 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.