Todd A. Kuiken, M.D., Ph.D., of the Rehabilitation Institute of Chicago and professor at Northwestern University, has designed a technique known as targeted muscle reinnervation (TMR), which lets a prosthetic arm respond directly to the brain's signals.
While the new device is easier to use than traditional motorized prosthetics, it still takes time to learn and is limited in the number of different types of movements that it can perform. Still under development, the device lets wearers open and close the artificial hands and bend or straighten the artificial elbow almost as naturally as their own arms.
“The idea is that when you lose your arm, you lose the motors, the muscles and the structural elements of the bones,” Kuiken explained. “But the control information should still be there in the residual nerves.”
Kuiken took the residual nerves that had previously carried the commands from the brain to produce movement and connected them to the chest muscles. This enables the signals to be sent and allows for movement in the artificial arm.
Science Daily reports the process as follows:
The signal is directed to a microprocessor in the artificial arm which decodes the signal and tells the arm what to do. In their work thus far, Kuiken and his colleagues have programmed the processor in the prosthetic arm to recognize four signals to produce two arm movements: open and close hand and bend and straighten elbow. When the patient thinks ‘close hand’ the hand closes. Contrast this with current motorized prosthetic arm technology: The patient has to learn to use new muscle groups to move the prosthetic arm; can perform only one movement at a time; and must contract two muscles at once to achieve a new movement. In the study published in the Journal of Neurophysiology*, they placed between 79-128 electrodes from the EMG onto the chest muscles of five patients to see if they could identify the unique EMG patterns emitted with 16 different elbow, wrist, hand, thumb and finger movements they asked the patients to perform. The EMG signals from each of the 16 movements were analyzed using advanced signal processing techniques. The study found that the researchers could recognize the signals associated with the different arm movements with 95% accuracy.
[Source: Science Daily]
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