# The Influence of Motion Data Low-Pass Filtering Methods in Machine-Learning Models  
[PERSON]  
1 Department of Physical Therapy, College of Applied Health Sciences, University of Illinois, Chicago, IL 60612, USA; [EMAIL_ADDRESS] (J.P.); [EMAIL_ADDRESS] (R.P)2 Ph.D. Program in Rehabilitation Sciences, Department of Physical Therapy, College of Applied Health Sciences, University of Illinois, Chicago, IL 60612, USA 2  
[PERSON]  
1 Department of Physical Therapy, College of Applied Health Sciences, University of Illinois, Chicago, IL 60612, USA; [EMAIL_ADDRESS] (J.P.); [EMAIL_ADDRESS] (R.P)2 Ph.D. Program in Rehabilitation Sciences, Department of Physical Therapy, College of Applied Health Sciences, University of Illinois, Chicago, IL 60612, USA 2  
[PERSON]  
1 Department of Physical Therapy, College of Applied Health Sciences, University of Illinois, Chicago, IL 60612, USA; [EMAIL_ADDRESS] (J.P.); [EMAIL_ADDRESS] (R.P)2 Ph.D. Program in Rehabilitation Sciences, Department of Physical Therapy, College of Applied Health Sciences, University of Illinois, Chicago, IL 60612, USA 2  
[PERSON]  
3 Department of Biological Sciences, University of Illinois, Chicago, IL 60612, USA; [EMAIL_ADDRESS] (J.P.); rpuroh@uic. filtering human motion data, and these parameters are often considered a \"standard\" in the field [8]. This standard approach was proven to be effective in removing noise for accurate gait parameter calculation and is widely utilized in numerous studies [9; 10; 11; 12]. While the Butterworth filter and these settings are now standard, other studies have suggested different parameters (order and cutoff) for the Butterworth filter during gait analysis [6; 13; 14; 15; 16], with filter orders ranging from 2 to 6 and cutoff frequencies ranging from 4 to 20 Hz. A few studies examined the impact of different filtering parameters for the Butterworth filter on human movement data analysis. Specifically, research showed that modifying filter settings can influence ground reaction force plate data, affecting key measures such as peak forces and loading rates during various tasks, including regular walking [17], terrain walking [18], and single-leg jump landings [19]. Additionally, alternative filters, such as Chebyshev or Bessel filters, have also been applied to motion data in previous studies [20; 21]. Compared to the Butterworth filter, the Chebyshev filter offers a faster roll-off due to its narrower transition bands. However, this comes at the cost of a passband ripple (fluctuations in the amplitude of the passband signals), which can introduce distortions that are undesirable in sensitive analyses like gait studies. The Bessel filter is a widely used filter in signal processing due to its superior phase linearity [22], indicating it can preserve the shape of a signal passing through it. However, the Bessel filter typically has a slower roll-off than the Butterworth and Chebyshev filters, making it less effective in attenuating high-frequency motion data noise. Therefore, the Butterworth filter remains the preferred choice for gait analysis due to its balance between effective noise attenuation, the preservation of amplitude consistency, and computational efficiency. However, it is not well understood if the standard low-pass Butterworth filter would still be suitable for non-traditional methods of gait analysis, such as in machine-learning models. Leveraging gait characteristics in machine-learning models for classification and prediction has gained popularity. Unlike traditional gait analysis that is highly sensitive to noise, some machine-learning methods (e.g., ensemble models) are generally more robust against noise. In fact, maintaining a small amount of noise in gait data might help to regularize these models by preventing overfitting and improving model generalization, thus enabling them to handle slight variations in input data more effectively.