Adaptive P-Splines for challenging filtering problems in biomechanics Article Swipe
Andrew J. Pohl
,
Matthew Schofield
,
W. Brent Edwards
,
Reed Ferber
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jbiomech.2024.112074
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jbiomech.2024.112074
Suppression of noise from recorded signals is a critically important data processing step for biomechanical analyses. While a wide variety of filtering or smoothing spline methods are available, the majority of these are not well suited for the analysis of signals with rapidly changing derivatives such as the processing of motion data for impact-like events. This is because commonly used low-pass filtering approaches or smoothing splines typically assume a single fixed cut-off frequency or regularization penalty which fails to describe rapid changes in the underlying function. To overcome these limitations we examine a class of adaptive penalized splines (APS) that extend commonly used penalized spline smoothers by inferring temporal adaptations in regularization penalty from observed data. Three variations of APS are examined in which temporal variation of spline penalization is described via either a series of independent random variables, an autoregressive process or a smooth cubic spline. Comparing the performance of APS on simulated datasets is promising with APS reducing RMSE by 48%-183% compared to a widely used Butterworth filtering approach. When inferring acceleration from noisy measurements describing the position of a pendulum impacting a barrier we observe between a 13% (independent variables) to 28% (spline) reduction in RMSE when compared to a 4th order Butterworth filter with optimally selected cut-off frequency. In addition to considerable improvement in RMSE, APS can provide estimates of uncertainty for fitted curves and generated quantities such as peak accelerations or durations of stationary periods. As a result, we suggest that researchers should consider the use of APS if features such as impact peaks, rates of loading, or periods of negligible acceleration are of interest.
Related Topics
Concepts
Smoothing
Spline (mechanical)
Smoothing spline
Mathematics
Algorithm
Autoregressive model
Penalty method
Computer science
Mean squared error
Regularization (linguistics)
Statistics
Mathematical optimization
Artificial intelligence
Spline interpolation
Engineering
Structural engineering
Bilinear interpolation
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jbiomech.2024.112074
- OA Status
- hybrid
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393931630
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393931630Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jbiomech.2024.112074Digital Object Identifier
- Title
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Adaptive P-Splines for challenging filtering problems in biomechanicsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-01Full publication date if available
- Authors
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Andrew J. Pohl, Matthew Schofield, W. Brent Edwards, Reed FerberList of authors in order
- Landing page
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https://doi.org/10.1016/j.jbiomech.2024.112074Publisher landing page
- Open access
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.jbiomech.2024.112074Direct OA link when available
- Concepts
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Smoothing, Spline (mechanical), Smoothing spline, Mathematics, Algorithm, Autoregressive model, Penalty method, Computer science, Mean squared error, Regularization (linguistics), Statistics, Mathematical optimization, Artificial intelligence, Spline interpolation, Engineering, Structural engineering, Bilinear interpolationTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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