An application of principal component analysis for lower body kinematics between loaded and unloaded walking

Lee, Minhyung; Roan, Michael J.; Smith, Benjamin

doi:10.1016/j.jbiomech.2009.06.052

Cited by 46 publications

(48 citation statements)

References 29 publications

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“…Principal components analysis (PCA) has been applied to biomechanics [Courtine et al, 2009;Lee et al, 2009] and EMG activity analysis [Widmer et al, 2003;Ivanenko et al, 2004;Stoloff et al, 2007]. It was used here to evaluate differences in locomotor EMG activity profiles between muscles and postdenervation time points.…”

Section: Principal Components Analysismentioning

confidence: 99%

Effect of Axon Misdirection on Recovery of Electromyographic Activity and Kinematics after Peripheral Nerve Injury

Sabatier¹,

To²,

Nicolini³

et al. 2011

Cells Tissues Organs

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In this study, patterns of activity in the soleus (Sol) and tibialis anterior (TA) muscles and hindlimb kinematics were evaluated during slope walking in rats after transection and surgical repair either of the entire sciatic nerve (Sci group) or of its two branches separately, the tibial and common fibular nerves (T/CF group). With the latter method, axons from the tibial and common fibular nerves could not reinnervate targets of the other nerve branch after injury, reducing the opportunity for misdirection. Activity in the TA shifted from the swing phase in intact rats to nearly the entire step cycle in both injured groups. Since these changes occur without misdirection of regenerating axons, they are interpreted as centrally generated. Sol activity was changed from reciprocal to that of TA in intact rats to coactivate with TA, but only in the Sci group rats. In the T/CF group rats, Sol activity was not altered from that observed in intact rats. Despite effects of injury that limited foot movements, hindlimb kinematics were conserved during downslope walking in both injury groups and during level walking in the T/CF group. During level walking in the Sci group and during upslope walking in both groups of injured rats, the ability to compensate for the effects of the nerve injury was less effective and resulted in longer limb lengths held at more acute angles throughout the step cycle. Changes in limb movements occur irrespective of axon misdirection and reflect compensatory changes in the outputs of the neural circuits that drive locomotion.

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Section: Principal Components Analysismentioning

confidence: 99%

Effect of Axon Misdirection on Recovery of Electromyographic Activity and Kinematics after Peripheral Nerve Injury

Sabatier¹,

To²,

Nicolini³

et al. 2011

Cells Tissues Organs

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show abstract

“…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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“…In addition, no such study has been carried out regarding Malaysian military subject to the matter discussed. Researchers conducted regarding load analysis include load magnitude, spine injury and posture [8], [20], [23], effects of load distribution [5], load posture and gait [2], load and GRF [3], [13], rifle carriage and human gait [4], load carriage effects on lower limb [29], soldiers load carriage [15], determining optimum load [10], [22], postural sway [11], load and walking gait [12], [17], [18] and load and trunk movements [16], [21]. Malaysian military soldiers with usually smaller build-up size without exception have to carry heavy load up to 50 kg or more.…”

Section: Introductionmentioning

confidence: 99%

“…FDA has been applied to various fields such as in biology and chemistry [28], plankton analysis [14], lactate curves in Biology and Medicine [19], gait analysis [27] and kinematic data [6]. Other methods like one-way ANOVA [22], one-way MANOVA [3], principal component analysis [17] and many more have been used in analysing load data but not FDA. Prior to this paper, an analysis for trial experiment on load GRF data by using FDA has been completed [7].…”

Section: Introductionmentioning

confidence: 99%

Load carriage analysis for military using functional data analysis technique : registration and permutation test

Din

Rambely

Jemain

2014

IJBAS

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This paper focused on the analysis of ground reaction force (GRF) of load carriage for military through functional data analysis (FDA) technique. The main objective of the study is to investigate the effect of 10% bodyweight (BW) load increment for normal walking through FDA method. The experiment on GRF was carried out by ten healthy military soldiers aged 31 ± 6.2 years old; weighing 71.6 ± 10.4 kg with the height of 166.3 ± 5.9 cm. Data was captured using Vicon 1.4 motion analysis system, Kistler force plates and thirty nine body markers attached to the subjects. FDA method mainly B-spline basis formation, time normalization, smoothing, curves registration and permutation tests were applied to analyze the data. The results confirmed that FDA did provide a better technique of smoothing to produce quality data in an easy manner. From this paper we conclude that registration did significantly align all the curves according to curves features providing a better way of distinguishing the amplitude of load increment. Finally, permutation tests confirmed that 10% BW load increased from 0% BW to 40% BW, have significant effect on soldiers regarding their load carriage and GRF.

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An application of principal component analysis for lower body kinematics between loaded and unloaded walking

Cited by 46 publications

References 29 publications

Effect of Axon Misdirection on Recovery of Electromyographic Activity and Kinematics after Peripheral Nerve Injury

Effect of Axon Misdirection on Recovery of Electromyographic Activity and Kinematics after Peripheral Nerve Injury

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

Load carriage analysis for military using functional data analysis technique : registration and permutation test

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