Teager-Kaiser energy operator signal conditioning improves EMG onset detection

Area of Science:

• Biomechanics
• Biomedical Engineering
• Signal Processing

Background:

• Accurate identification of muscle activity onset is crucial for biomechanical analysis of human movement.
• Existing electromyography (EMG) onset detection methods can be limited in accuracy.
• Signal conditioning plays a key role in refining EMG data for analysis.

Purpose of the Study:

• To evaluate the impact of incorporating the Teager-Kaiser energy operator (TKEO) into signal conditioning.
• To determine if TKEO enhances the accuracy of common EMG onset detection techniques.
• To assess the clinical applicability of TKEO conditioning in gait analysis.

Main Methods:

• Three EMG onset detection methods were tested: visual determination, threshold-based, and approximated generalized likelihood ratio.
• These methods were applied to EMG signals with and without TKEO conditioning.
• Reference signals were created from isometric vastus lateralis contractions (n=17) and experimental gait data (n=255) were also used.

Main Results:

• TKEO conditioning significantly reduced the mean detection error for all three methods compared to non-TKEO conditioning.
• Error reduction was observed with both artificially generated reference data (13 ms vs. 98 ms) and experimental gait data (55 ms vs. 124 ms).
• Statistical analysis confirmed significant improvements (p < 0.001) with TKEO inclusion.

Conclusions:

• The addition of TKEO as a signal conditioning step demonstrably increases the accuracy of detecting EMG burst boundaries.
• TKEO offers a valuable enhancement for EMG analysis in biomechanics and clinical settings.
• This method improves the reliability of identifying the precise start of muscle activation.