Improved accuracy of biomechanical motion data obtained during impacts using a time-frequency low-pass filter

Area of Science:

• Biomechanics
• Movement Science
• Signal Processing

Background:

• Conventional filters (CFs) inadequately represent biomechanical motion data with impacts.
• Time-frequency filters (TFFs) offer a promising alternative but are underutilized in movement science.
• Accurate segmental kinematics are crucial for understanding human movement dynamics.

Purpose of the Study:

• To evaluate a modified fractional Fourier filter (MFrFF) for analyzing biomechanical impact data.
• To compare the performance of the MFrFF against conventional filters during football kicking.
• To assess the accuracy of MFrFF in capturing peak accelerations during foot-to-ball impact.

Main Methods:

• Modification of Georgakis and Subramaniam's (2009) fractional Fourier filter.
• Application of the modified filter (MFrFF) to lower leg accelerations during football instep kicking.
• Comparison of MFrFF results with conventional filters and a reference accelerometer.

Main Results:

• MFrFF demonstrated superior accuracy for peak accelerations during foot-to-ball impact (peak % error = -5.0 ± 11.4%).
• Conventional filters severely underestimated impact peaks (30-70% error).
• MFrFF performance was comparable to CFs during non-impact phases (RMSE = 37.3 ± 7.6 m/s² vs. 42.1 ± 11.4 m/s²).

Conclusions:

• The modified fractional Fourier filter (MFrFF) accurately captures high-acceleration impacts in biomechanics.
• MFrFF is a superior alternative to conventional filters for analyzing impact events.
• MFrFF should be applied to diverse biomechanical impact scenarios to improve research efficacy.