Psycho-physiological training approach for amputee rehabilitation.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Rehabilitation Technology

Background:

• Electromyography (EMG) signals are prone to noise and conventional analog processing methods suffer from instability.
• Surface EMG signals exhibit a wide frequency range (6Hz-600Hz), with dominant components between 20Hz-150Hz.

Purpose of the Study:

• To develop an effective and reliable digital signal processing (DSP) technique for analyzing EMG signals across their complete frequency range.
• To design a stable and cost-effective alternative to conventional analog amplification and filtering circuits for EMG signal acquisition.
• To explore the application of processed EMG signals in a psychophysiological approach for prosthesis rehabilitation.

Main Methods:

• Spectrum analysis was performed on an Arduino Uno board for amplification, filtering, and thresholding of EMG signals.
• Time-domain EMG signals were converted to the frequency domain to extract detailed signal characteristics.
• A digital signal processing approach was implemented to overcome the limitations of analog circuits.

Main Results:

• The developed DSP technique effectively processed EMG signals, removing noise and instability issues associated with analog circuits.
• The processed EMG signals were utilized to control an online game, demonstrating their viability for interactive applications.
• The study successfully analyzed EMG signals over their complete frequency range using a digital approach.

Conclusions:

• The proposed digital signal processing technique offers an easy, effective, and reliable method for EMG signal analysis.
• This approach can significantly reduce the cost and increase the efficiency of myoelectric prostheses.
• The psychophysiological approach using DSP EMG signals shows promise for enhancing the rehabilitation process by providing users with less stressful muscle control.