A Way of Bionic Control Based on EI, EMG, and FMG Signals

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Human-Machine Interfaces

Background:

• Developing advanced prosthetic, orthotic, and rehabilitation devices is crucial for societal well-being.
• Current limitations in intuitive control interfaces hinder the progress of these functional devices.
• Accurate interpretation of biological signals from extremities is key for effective device control.

Purpose of the Study:

• To investigate the simultaneous acquisition and analysis of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals.
• To assess the potential of these combined signals for controlling prosthetic and rehabilitation devices.
• To evaluate the feasibility of real-time anthropomorphic and proportional control strategies.

Main Methods:

• Simultaneous recording of EI, EMG, and FMG signals during basic wrist movements (grasping, flexion/extension, rotation).
• Development of a laboratory instrumentation and software test setup for data acquisition.
• Analysis of the acquired multimodal biological signals.

Main Results:

• Electric impedance (EI) signals, when analyzed alongside EMG and FMG, show high potential for anthropomorphic control systems.
• The comprehensive real-time analysis of EI, EMG, and FMG signals is feasible.
• This multimodal approach enables anthropomorphic and proportional control with acceptable delays.

Conclusions:

• Combining EI, EMG, and FMG signals provides a rich dataset for advanced prosthetic control.
• The findings support the development of more intuitive and responsive prosthetic, orthotic, and rehabilitation devices.
• Real-time analysis of these signals paves the way for improved human-machine interaction in assistive technologies.