Clinical implementation of a bionic hand controlled with kineticomyographic signals

Area of Science:

• Biomedical Engineering
• Neuroprosthetics
• Surgical Innovation

Background:

• Traditional prosthetic control methods like EMG signals face challenges with noise and intuitive movement.
• Magnetic tag sensing offers a potential alternative but has been limited to simulations due to invasiveness.
• Clinical implementation of magnetic tags for prosthetic control is lacking.

Purpose of the Study:

• To report the first clinical implementation of surgically implanted magnetic tags for prosthetic hand control.
• To introduce KineticoMyoGraphy (KMG) for extracting human intention from magnetic fields.
• To evaluate the accuracy and utility of this novel sensing interface.

Main Methods:

• A flexor-extensor tendon transfer surgical procedure was used to implant magnetic tags.
• Artificial neural networks were employed to interpret KMG signals, bypassing magnet localization.
• A game-based strategy was utilized for algorithm testing and patient rehabilitation.

Main Results:

• The study successfully implemented implanted magnetic tags for prosthetic hand control in an amputee.
• KineticoMyoGraphy (KMG) effectively extracted human intention from magnetic fields.
• Statistical analysis confirmed the accuracy of the magnetic tag interface in recognizing intended gestures and movement grades.

Conclusions:

• Surgically implanted magnetic tags represent a viable and accurate sensing interface for prosthetic hand control.
• KineticoMyoGraphy (KMG) offers a promising approach for intuitive and robust prosthetic limb function.
• This innovative method bridges medical and engineering perspectives for improved amputee rehabilitation and bionic hand integration.