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After a leg amputation, walking with prosthetic limb is especially difficult for people who have lost their leg above the knee. Robotic prostheses that provide power are being developed and may help people walk with less effort. However controlling these devices is not easy. Onboard mechanical sensors provide information to tell the prosthesis what to do at each stage of walking, but the user cannot easily tell the prosthesis what they want it to do.

Electrical signals, called EMG signals, are produced when a muscle contracts. For the past decade, scientists at The Regenstein Center for Bionic Medicine have been researching ways to harness information from these signals to predict what the user wants to do. Combining leg muscle EMG signals with mechanical sensor information makes the control system work better and feel more natural. Unfortunately, recording EMG signals from the surface of the skin is difficult ¡ª any change in the skin (such as sweating) or location of the electrode affects the reliability of the signals. A control system that relies on varying signals may not work well and may cause falls. One way to get more stable signals is to record EMG from inside the muscle. This can be done by implanting special sensors (MyoNodes) into leg muscles. MyoNodes can transmit EMG information wirelessly to a base station on the prosthesis.

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The goal of this project is to compare the control information provided by surface EMG to that obtained from inside the muscle (intramuscular EMG). We anticipate that EMG from inside the muscle will be more consistent and reliable than EMG recorded at the skin surface.

This study will help us learn if intramuscular EMG is better for controlling a powered prosthesis. Also, because the muscles that used to control the ankle are missing after a leg amputation, we will also surgically transfer nerves that used to control those muscles to new muscles in the leg, using a technique called targeted muscle reinnervation (TMR). Once the nerves have grown into the new muscles, these muscles generate EMG when the person tries to move their ankle. We will use EMG signals from reinnervated muscles to control the prosthetic ankle and EMG from hip flexors (that lift the leg) and see if this improves the person¡¯s walking pattern and energy requirements when using a powered prosthesis.

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Levi Hargrove, PhD, Principal Investigator

Ann Simon, PhD, Engineering Manager

Andrea Ikeda, CP, Research Prosthetist

Suzanne Finucane, PTA, MS, CCRC, Operations Manager

Kevin Brenner, MS, Biomedical Research Engineer

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Restoring Warfighters with Neuromusculoskeletal Injuries Research Award (RESTORE) - Congressionally Directed Medical Research Programs