Smoothing spline analysis of variance models: A new tool for the analysis of cyclic biomechanical data

Helwig, Nathaniel E.; Shorter, K. Alex; Ma, Ping; Hsiao-Wecksler, Elizabeth T.

doi:10.1016/j.jbiomech.2016.07.035

Cited by 24 publications

(14 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where g is the number of comparisons (g = 6). Significance was defined when the adjusted 95% CI of the mean pairwise group CCD does not contain zero 43 .…”

Section: Ucm Analysis-partmentioning

confidence: 99%

Influence of low back pain and its remission on motor abundance in a low-load lifting task

Liew

Nunzio

Srivastava

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Having an abundance of motor solutions during movement may be advantageous for the health of musculoskeletal tissues, given greater load distribution between tissues. The aim of the present study was to understand whether motor abundance differs between people with and without low back pain (LBP) during a low-load lifting task. Motion capture with electromyography (EMG) assessment of 15 muscles was performed on 48 participants [healthy control (con) = 16, remission LBP (rLBP) = 16, current LBP (cLBP) = 16], during lifting. Non-negative matrix factorization and uncontrolled manifold analysis were performed to decompose inter-repetition variability in the temporal activity of muscle modes into goal equivalent (GEV) and non-goal equivalent (NGEV) variabilities in the control of the pelvis and trunk linear displacements. Motor abundance occurs when the ratio of GEV to NGEV exceeds zero. There were significant group differences in the temporal activity of muscle modes, such that both cLBP and rLBP individuals demonstrated greater activity of muscle modes that reflected lumbopelvic coactivation during the lifting phase compared to controls. For motor abundance, there were no significant differences between groups. Individuals with LBP, including those in remission, had similar overall motor abundance, but use different activation profiles of muscle modes than asymptomatic people during lifting.

show abstract

“…where g is the number of comparisons (g = 6). Significance was defined when the adjusted 95% CI of the mean pairwise group CCD does not contain zero 43 .…”

Section: Ucm Analysis-partmentioning

confidence: 99%

Influence of low back pain and its remission on motor abundance in a low-load lifting task

Liew

Nunzio

Srivastava

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…We can cite [7] in which curve clustering is used to analyse the foot-strike of runners, or [8] for the study of muscle fatigue through a whole FDA analysis. Another example is given in [9] that proposes a functional version of ANOVA using splines to overcome common issues that occur in sport medicine. Finally, the work presented in [10] uses curve clustering methods to study different types of footfall in running.…”

Section: Longitudinal Data In Sportmentioning

confidence: 99%

Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

et al. 2018

View full text Add to dashboard Cite

Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.

show abstract

“…Smoothing spline ANOVA (SSANOVA) models are widely used in applications [11,20,36,37]. In this chapter, we introduced the general framework of the SSANOVA models in Section 2.…”

Section: Resultsmentioning

confidence: 99%

Smoothing Spline ANOVA Models and their Applications in Complex and Massive Datasets

Zhang¹,

Jin²,

Wang³

et al. 2018

Topics in Splines and Applications

Self Cite

View full text Add to dashboard Cite

Complex and massive datasets can be easily accessed using the newly developed data acquisition technology. In spite of the fact that the smoothing spline ANOVA models have proven to be useful in a variety of fields, these datasets impose the challenges on the applications of the models. In this chapter, we present a selected review of the smoothing spline ANOVA models and highlight some challenges and opportunities in massive datasets. We review two approaches to significantly reduce the computational costs of fitting the model. One real case study is used to illustrate the performance of the reviewed methods.

show abstract

Smoothing spline analysis of variance models: A new tool for the analysis of cyclic biomechanical data

Cited by 24 publications

References 31 publications

Influence of low back pain and its remission on motor abundance in a low-load lifting task

Influence of low back pain and its remission on motor abundance in a low-load lifting task

Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

Smoothing Spline ANOVA Models and their Applications in Complex and Massive Datasets

Contact Info

Product

Resources

About