Search Results

You are looking at 1 - 10 of 137 items for :

  • "principal component analysis" x
Clear All
Restricted access

Igor Ramathur Telles Jesus, Roger Gomes Tavares Mello and Jurandir Nadal

During muscle fatigue analysis some standard indexes are calculated from the surface electromyogram (EMG) as root mean square value (RMS), mean (Fmean), and median power frequency (Fmedian). However, these parameters present limitations and principal component analysis (PCA) appears to be an adequate alternative. In this context, we propose two indexes based on PCA to enhance the quantitative muscle fatigue analysis during cyclical contractions. Signals of vastus lateralis muscle were collected during a maximal exercise test. Twenty-four subjects performed the test starting at 12.5 W power output with increments of 12.5 W⋅min–1, maintaining cadence of 50 rpm until voluntary exhaustion. The epochs of myoelectric activation were identified and used to estimate the power spectra. PCA was then applied to the power spectra of each subject. The standard (ST) and Euclidean (ED) distances were employed to estimate the alteration occurred due to fatigue. For comparison, the standard indexes were calculated. ST, ED, and RMS value were adequate for muscle fatigue analysis. Among these parameters, ST was more sensitive with higher effect size. Moreover, the Fmean and Fmedian were not sensitive to fatigue. The proposed method based on PCA of EMG in frequency domain allowed producing fatigue indexes suitable for cyclical contractions.

Restricted access

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

Restricted access

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

Restricted access

Grant E. Norte, Jay N. Hertel, Susan A. Saliba, David R. Diduch and Joseph M. Hart

assessment program, we aim to identify tests that provide the most meaningful information about a population of interest. Principal component analysis (PCA) is an analytical technique that can help in this regard by reducing the dimensionality of a larger set of measures to provide a clearer interpretation

Restricted access

Dan Weaving, Nicholas E. Dalton, Christopher Black, Joshua Darrall-Jones, Padraic J. Phibbs, Michael Gray, Ben Jones and Gregory A.B. Roe

translate this information into actionable manipulation of the training process. 5 , 18 One such capable approach is principal component analysis (PCA), which attempts to explain the maximal amount of information (ie, variance) within a data set that consists of multiple variables, such as those often found

Restricted access

Peter S. Myers, Kerri S. Rawson, Elinor C. Harrison, Adam P. Horin, Ellen N. Sutter, Marie E. McNeely and Gammon M. Earhart

, particularly in freezers, 2 interventions to reduce gait variability are limited. 15 This may be because primary sources of gait variability remain unclear. One method for understanding variability sources is principal component analysis (PCA), which separates total variance into independent principal

Restricted access

Denise M. Rossi, Renan A. Resende, Gisele H. Hotta, Sérgio T. da Fonseca and Anamaria S. de Oliveira

Thus, the greatest challenge in the time series analysis of biomechanical data is the selection of the most relevant characteristics due to the multidimensionality, collinearity, and variability of the data. 23 Principal component analysis is an alternative approach to generate summary parameters of

Restricted access

Rachael L. Thurecht and Fiona E. Pelly

questionnaire development and utilizes both theoretical knowledge and statistical analysis to devise a more concise question set into meaningful component factors ( Pett et al., 2003 ). A principal component analysis (PCA), a form of exploratory factor analysis, was selected to reduce the 84 food choice items

Restricted access

Christopher M. Saliba, Allison L. Clouthier, Scott C.E. Brandon, Michael J. Rainbow and Kevin J. Deluzio

predicted medial and lateral contact loads for a single subject to within 0.3 bodyweight of in vivo measured loads and estimated changes in contact loading caused by gait perturbation within 0.1 bodyweight. 23 Principal component analysis can be used to represent gait waveform variables as a linear

Restricted access

Hee Sik Kim and Kiyoji Tanaka

The purpose of this study was to assess the extent to which a battery of 24 activities of daily living (ADL) performance tasks could be used to determine functional age in a sample of older women. The subjects were 253 older adult Korean women, aged 60 to 91 years. All subjects completed a comprehensive battery of 24 performance tests related to common activities of daily living. Correlations between the measures were computed, and principal component analysis was applied to the 24 × 24 correlation matrix. A principal component score was computed for each subject and was found to decrease significantly with advancing age. Multiple regression analysis revealed that out of the initial 24 variables, 5 variables accounted for 81% of the variability. An equation was developed to determine ADL age; the equation was considered useful for the assessment of daily living activities of older adult Korean women.