Assessment of wavelet analysis of gait in children with typical development and cerebral palsy

Lauer, Richard T.; Stackhouse, Carrie; Shewokis, Patricia A.; Smith, Brian T.; Orlin, Margo; McCarthy, James J.

doi:10.1016/j.jbiomech.2004.07.002

Cited by 36 publications

(31 citation statements)

References 41 publications

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“…In these applications, one PCA is often conducted for each joint/muscle/segment using a subjects × time matrix, where the subjects mode is a collection of subjects from different groups (e.g., Astephen et al, 2008;Chester & Wrigley, 2008;Cochran et al, 1984;Donoghue et al, 2008;Lamoth et al, 2006;Lauer et al, 2005;Lee et al, 2009;McKean et al, 2007). The PCs from these separate analyses are then examined to understand gait patterns of different subject groups, and PC scores (see Section 2.1.1) are often used to quantify between-group differences.…”

Section: Introductionmentioning

confidence: 99%

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Helwig

Hong

Polk

2012

Human Movement Science

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Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three-or higher-mode data. Using three-mode joint angle waveform data (subjects × time × joints), we demonstrate Parafac's ability to (a) determine interpretable components revealing the primary interrelationships between lowerlimb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects' gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).ii To my loving and supportive family.iii

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Section: Introductionmentioning

confidence: 99%

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Helwig

Hong

Polk

2012

Human Movement Science

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“…A recent report has demonstrated that wavelet decomposition of EMG signals coupled to principal component analysis can identify the variable nature of EMG mean frequencies in cerebral palsy [10], and it was suggested that it may be possible to use the EMG signals to derive a physiologically based quantitative index for assessing motor function. Thus, www.elsevier.com/locate/gaitpost Gait & Posture 25 (2007) [580][581][582][583][584][585][586][587][588][589] the purpose of this study was to determine whether information about spectral shape, intensity, and co-activations between muscles could be incorporated into time-frequency analyses of the EMG signals, in order to develop a technique for quantifying the degree of dysfunction experienced by particular muscles in children with CP.…”

Section: Introductionmentioning

confidence: 99%

A method for quantifying dynamic muscle dysfunction in children and young adults with cerebral palsy

Wakeling¹,

Delaney²,

Dudkiewicz³

2007

Gait & Posture

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“…Once the maximum voluntary force -length -velocity surface for the muscle had been obtained then determining the SEMG over the same phase space with suitable resolutions would seem a logical corollary. Although the CWT has previously been used for processing SEMG signals (Pope et al, 2000;Karlsson et al, 2001;Lauer et al, 2005) little information existed on the choice of wavelet shape. Discriminative SEMG amplitude and frequency data throughout the phase space were only obtained after careful selection of this parameter.…”

Section: Discussionmentioning

confidence: 99%

“…The SEMG signals from dynamic contractions are non-stationary requiring their spectral properties to be determined using a combined time -frequency method (Karlsson et al, 2000). Traditionally the short time Fourier transform method has been employed (Gerdle et al, 1988;Komi et al, 2000), however more recently the continuous wavelet transform (CWT, described in Qian, 2002) has been used in a number of SEMG studies (Karlsson et al, 2000;Pope et al, 2000;von Tscharner, 2000;Lauer et al 2005). The CWT is suggested to provide a better time -frequency resolution trade-off for SEMG signals based on the range of their frequency content (Akay, 1998;Karlsson et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

A Combined Muscle Model and Wavelet Approach to Interpreting the Surface EMG Signals from Maximal Dynamic Knee Extensions

Forrester¹,

Pain²

2010

Journal of Applied Biomechanics

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This study aimed to identify areas of reduced surface EMG amplitude and changed frequency across the phase space of a maximal dynamic knee extension task. The hypotheses were: (1) amplitude would be lower for eccentric contractions compared to concentric contractions and unaffected by fibre length; and (2) mean frequency would also be lower for eccentric contractions and unaffected by fibre length. Joint torque and EMG signals from the vastii and rectus femoris were recorded for eight athletic subjects performing maximum knee extensions at thirteen joint velocities spanning ±250° s -1 . The instantaneous amplitude and mean frequency were calculated using the continuous wavelet transform time -frequency method, and the fibre dynamics were determined using a muscle model of the knee extensions. The results indicated: (1) only for the rectus femoris were amplitudes significantly lower for eccentric contractions (p = 0.019), for the vastii amplitudes during eccentric contractions were less than maximal, but this was also the case for concentric contractions due to a significant reduction in amplitude towards knee extension (p = 0.023); and (2) mean frequency increased significantly with decreasing fibre length for all knee extensors and contraction velocities (p = 0.029). Using time -frequency processing of the EMG signals and a muscle model allowed the simultaneous assessment of fibre length, velocity and EMG.

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Assessment of wavelet analysis of gait in children with typical development and cerebral palsy

Cited by 36 publications

References 41 publications

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

A method for quantifying dynamic muscle dysfunction in children and young adults with cerebral palsy

A Combined Muscle Model and Wavelet Approach to Interpreting the Surface EMG Signals from Maximal Dynamic Knee Extensions

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