EFFECT OF FILTERING KINEMATICS ON FINITE ELEMENT SIMULATIONS OF HEAD IMPACTS IN HIGH SCHOOL FEMALE LACROSSE

Area of Science:

• Biomechanics
• Sports Medicine
• Sensor Technology

Background:

• Consensus Head Acceleration Measurement Practices (CHAMP) recommends reporting filter characteristics for head impact sensors.
• Specific filter selection depends on device and application, with no universal standard prescribed.
• Previous research used Stanford Instrumented Mouthguard (MiG) sensors to record impacts in high school female lacrosse players.

Purpose of the Study:

• To investigate the impact of different low-pass filter cut-off frequencies on head kinematics data from instrumented mouthguards.
• To assess the influence of filtering on simulated brain stresses and strains using a finite element (FE) head model.

Main Methods:

• Raw kinematics data from 14 video-verified impacts were re-filtered at 50, 100, and 200 Hz cut-off frequencies.
• Peak linear and angular accelerations and velocities were compared across different filter settings.
• Filtered kinematics were used to drive an FE human head model to compare 95th percentile brain stresses and strains.

Main Results:

• Lowering the low-pass filter cut-off frequency substantially reduced peak linear and angular accelerations.
• Peak angular velocity was less affected by changes in filter cut-off frequency.
• Reduced filtering cut-off frequencies led to lower 95th percentile stresses and strains within the simulated brain.

Conclusions:

• Low-pass filtering significantly impacts head kinematics data and subsequent injury metric calculations.
• Over-filtering can lead to loss of important signal information, potentially underestimating impact severity.
• Careful consideration of filtering methods is crucial for future studies using instrumented mouthguard data and FE head models.