Adaptive wavelet EMG compression based on local optimization of filter banks

Area of Science:

• Biomedical Engineering
• Signal Processing
• Data Compression

Background:

• Surface electromyographic (sEMG) signals are crucial for biomechanical analysis.
• Efficient compression of sEMG data is vital for storage and transmission.
• Existing wavelet compression techniques may introduce significant signal distortion.

Purpose of the Study:

• To develop an adaptive wavelet technique for sEMG signal compression.
• To minimize distortion in compressed sEMG signals using an optimization algorithm.
• To evaluate the performance of the proposed technique against standard methods.

Main Methods:

• An adaptive wavelet technique was developed for sEMG signal compression.
• An optimization algorithm was used to adjust the wavelet filter bank.
• Orthogonality was maintained using a restriction-free parametrization.
• The technique was validated using real-life isotonic and isometric sEMG signals.

Main Results:

• The proposed adaptive wavelet technique minimized signal distortion.
• The optimized approach demonstrated superior performance compared to non-optimized methods.
• Percent residual difference was significantly lower for the adaptive technique at given compression factors.

Conclusions:

• The adaptive wavelet technique offers improved compression for sEMG signals.
• Optimization of wavelet filter banks enhances compression quality.
• This method provides a more efficient approach for handling sEMG data.