A method for the control of multigrasp myoelectric prosthetic hands

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Human-Computer Interaction

Background:

• Myoelectric control is crucial for advanced prosthetic limbs.
• Existing systems often lack intuitive control for complex hand movements.
• Proportional control of multigrasp prosthetics remains a significant challenge.

Purpose of the Study:

• To design and validate a novel multigrasp myoelectric controller.
• To enable direct and proportional control of prosthetic hand postures.
• To compare the efficacy of the myoelectric controller against a data glove for virtual prosthesis control.

Main Methods:

• Developed a multigrasp myoelectric controller using two surface electromyography electrodes.
• Implemented direct and proportional control for nine characteristic prosthetic hand postures.
• Conducted experiments with five nonamputee subjects controlling a virtual prosthesis using the myoelectric controller and a data glove.

Main Results:

• The multigrasp myoelectric controller achieved an average transition time of 1.49 seconds between postures.
• The native hand using a data glove achieved an average transition time of 0.81 seconds.
• Both control methods demonstrated high transition completion rates, averaging 99.2%.

Conclusions:

• The multigrasp myoelectric controller offers a viable method for controlling prosthetic hand motion.
• While slightly slower than native hand control, it provides comparable accuracy.
• Preliminary validation suggests potential for intuitive and effective prosthetic hand operation.