Principal component analysis and functional regression are combined in a model to analyze a time series of pressure maps. The model is tested measuring the pressures over a chair seat while a subject performs a combination of simple movements. A sampling rate of 3 Hz is adequate for applying the model in sitting postures. The model is able to detect patterns of movement over time, although more variables are necessary if the movements produce similar pressure distributions.
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Juan C. Chicote, Juan V. Durá, Juan M. Belda, and Rakel Poveda
Samuel Ryan, Thomas Kempton, and Aaron J. Coutts
selectively reduce the number of variables that are collected and analyzed to improve the efficiency of analysis without losing the veracity of the information provided by these data. One such method is principal-component analysis (PCA), a data reduction technique designed to evaluate the contribution of
Jianning Wu and Jue Wang
In this technical note, we investigate a combination PCA with SVM to classify gait pattern based on kinetic data. The gait data of 30 young and 30 elderly participants were recorded using a strain gauge force platform during normal walking. The gait features were first extracted from the recorded vertical directional foot– ground reaction forces curve using PCA, and then these extracted features were adopted to develop the SVM gait classifier. The test results indicated that the performance of PCA-based SVM was on average 90% to recognize young– elderly gait patterns, resulting in a markedly improved performance over an artificial neural network–based classifier. The classification ability of the SVM with polynomial and radial basis function kernels was superior to that of the SVM with linear kernel. These results suggest that the proposed technique could provide an effective tool for gait classification in future clinical applications.
I-Chieh Lee, Yeou-Teh Liu, and Karl M. Newell
We investigated the coordination of balance and propulsion processes in learning to ride a unicycle through a principal component analysis (PCA) of the nature and number of functional degrees of freedom (DOF) in the movement coordination patterns. Six participants practiced unicycle riding on an indoor track for 28 sessions over separate days. The movement time and performance outcomes were recorded for each trial and body segment kinematics were collected from the first and every succeeding 4th session. The first appearance of no-hand-support performance varied across participants from the 5th practice session to the 22nd session. The PCA showed that initially in practice the 39 kinematic time series could be represented by 6–9 components that were reduced over practice to 4–7 components. The loadings of the PCA that reflected balance and propulsion processes became more coupled as a function of successfully riding the unicycle. The findings support the proposition that learning to ride the unicycle is a process of making the system more controllable by coordinating balance and propulsion while mastering the redundant DOF.
Michal Vagner, Daniel J. Cleather, Petr Kubový, Vladimír Hojka, and Petr Stastny
component analysis (PCA) can be used to identify fundamental patterns that describe a large proportion of the variability seen in vertical jumping ( Cleather & Cushion, 2019 ; Cushion et al., 2019 , 2020 ). We have recently shown that there are kinetic and kinematic differences in the front kicking
Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Marco Cardinale, and Aki I.T. Salo
Purpose:
An extensive battery of physical tests is typically employed to evaluate athletic status and/or development, often resulting in a multitude of output variables. The authors aimed to identify independent physical predictors of elite skeleton start performance to overcome the general problem of practitioners employing multiple tests with little knowledge of their predictive utility.
Methods:
Multiple 2-d testing sessions were undertaken by 13 high-level skeleton athletes across a 24-wk training season and consisted of flexibility, dry-land push-track, sprint, countermovement-jump, and leg-press tests. To reduce the large number of output variables to independent factors, principal-component analysis (PCA) was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple-regression analysis, and K-fold validation assessed model stability.
Results:
PCA revealed 3 components underlying the physical variables: sprint ability, lower-limb power, and strength–power characteristics. Three variables that represented these components (unresisted 15-m sprint time, 0-kg jump height, and leg-press force at peak power, respectively) significantly contributed (P < .01) to the prediction (R 2 = .86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs actual R 2 = .77; 1.97% standard error of estimate).
Conclusions:
Only 3 physical-test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring, potentially benefitting sport scientists, coaches, and athletes alike.
Alis Bonsignore, David Field, Rebecca Speare, Lianne Dolan, Paul Oh, and Daniel Santa Mina
Prostate cancer (PCa) is the leading nonskin cancer diagnosis among males. 1 Fortunately, advances in detection and treatment have led to earlier diagnosis and treatment, improving 5-year survival rates to over 90%. 2 However, this survivorship period is often fraught with side effects from
Auke A. Post, Gert de Groot, Andreas Daffertshofer, and Peter J. Beek
In mechanical studies of pumping a playground swing, two methods of energy insertion have been identified: parametric pumping and driven oscillation. While parametric pumping involves the systematic raising and lowering of the swinger’s center of mass (CM) along the swing’s radial axis (rope), driven oscillation may be conceived as rotation of the CM around a pivot point at a fixed distance to the point of suspension. We examined the relative contributions of those two methods of energy insertion by inviting 18 participants to pump a swing from standstill and by measuring and analyzing the swing-swinger system (defined by eight markers) in the sagittal plane. Overall, driven oscillation was found to play a major role and parametric pumping a subordinate role, although the relative contribution of driven oscillation decreased as swinging amplitude increased, whereas that of parametric pumping increased slightly. Principal component analysis revealed that the coordination pattern of the swing-swinger system was largely determined (up to 95%) by the swing’s motion, while correlation analysis revealed that (within the remaining 5% of variance) trunk and leg rotations were strongly coupled.
Dan Weaving, Clive Beggs, Nicholas Dalton-Barron, Ben Jones, and Grant Abt
insights communicated to coaches. In this regard, the use of dimension reduction techniques, such as principal component analysis (PCA) 11 , 19 and single value decomposition (SVD), 20 are gaining popularity within sports performance research. For example, PCA and SVD have been used in studies examining
Lauren C. Benson, Stephen C. Cobb, Allison S. Hyngstrom, Kevin G. Keenan, Jake Luo, and Kristian M. O’Connor
clearance throughout swing for people with a variety of walking patterns, and especially those at risk for falling, is warranted. A principal components analysis (PCA) approach to quantifying foot clearance and foot clearance variability may resolve these issues. PCA can be used to identify modes of
