Functional Data Analyses for the Assessment of Joint Power Profiles During Gait of Stroke Subjects

Andrade, André Gustavo Pereira de; Polese, Janaíne Cunha; Paolucci, Leopoldo Augusto; Menzel, Hans-Joachim; Teixeira-Salmela, Luci Fuscaldi

doi:10.1123/jab.2013-0147

Cited by 15 publications

(11 citation statements)

References 21 publications

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“…We plotted our estimates of pairwise comparison functions between pre-and post-fatigue, as well as 95 % confidence interval (CI) bands to determine significant differences. Differences were identified where the curve plus its effect size (95 % CI) did not cross the zero line [1]. We used the fda package in the statistical program "R" (version 2.15.1) for all FANOVA s.…”

Section: Discussionmentioning

confidence: 99%

Functional Fatigue Alters Lower-extremity Neuromechanics during a Forward-side Jump

et al. 2015

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Neuromuscular fatigue impairs neuromuscular control of the lower extremity. The purpose of the study was to investigate the effect of functional fatiguing exercises on sagittal-plane lower-extremity neuromechanics during a forward-side jump. 21 participants performed 5 forward-side jump tasks before and after functional fatiguing exercises. A functional analysis of variance (FANOVA) evaluated differences between 2 different conditions (pre- vs. post-fatigue) for joint angle, moment, and EMG amplitude during stance of a forward-side jump. FANOVA compared variables as polynomial functions, and differences between functions with 95% confidence interval bands were plotted to determine significant differences. Plantar, knee, and hip flexion decreased during the initial stages of landing following fatigue. Plantarflexion moment decreased during 10-20% of stance in post-fatigue. Knee extension moment initially increased while decreased during 20-30% of stance following fatigue. Hip extension moment initially decreased while increased at 20% of stance. Tibialis anterior EMG decreased during 30-40% of stance, vastus lateralis EMG increased at 15% of stance, hamstring EMG decreased at foot contact and during 25-60% of stance, and gluteus maximus EMG decreased at foot contact and 35% of stance. The functional fatiguing exercises resulted in a more upright landing position, potentially indicating a greater reliance upon skeletal structures for support.

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Section: Discussionmentioning

confidence: 99%

Functional Fatigue Alters Lower-extremity Neuromechanics during a Forward-side Jump

et al. 2015

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“…Analysis of an ensemble of curves and other comparisons of data over time is not a completely novel concept, but the application strength and sophistication used in the analysis varies. A recent paper describing a functional data analysis of variance approach (FANOVA) also used B-spline basis functions ( Andrade et al, 2014 ). This paper proposed the simple effects model with one factor, as in y jy ( t ) = μ ( t ) + α j ( t ) + ε jy ( t ), where μ ( t ) is the overall functional mean and is the effect of the jth level of Factor A.…”

Section: Discussionmentioning

confidence: 99%

“…We also conducted three separate functional analyses for the sagittal plane ankle, knee and hip joint angles (R 2.15.1, R development core team, http://madison.byu.edu/FDA.html ). We used a modified version of a functional data analysis procedure ( Andrade et al, 2014 ). This requires use of basis functions, warping and a statistical framework, as described below.…”

Section: Methodsmentioning

confidence: 99%

“…We introduced and explained the key applications of a functional data analysis for human movement data. The functional data analysis procedure used here is a modification of the previous model ( Andrade et al, 2014 ). Our approach considers a more general cell means model version of the previous approach that allows for a general treatment structure of multiple factors.…”

Section: What Does This Article Add?mentioning

confidence: 99%

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Functional vs. Traditional Analysis in Biomechanical Gait Data: An Alternative Statistical Approach

Park

Seeley

Francom

et al. 2017

Journal of Human Kinetics

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In human motion studies, discrete points such as peak or average kinematic values are commonly selected to test hypotheses. The purpose of this study was to describe a functional data analysis and describe the advantages of using functional data analyses when compared with a traditional analysis of variance (ANOVA) approach. Nineteen healthy participants (age: 22 ± 2 yrs, body height: 1.7 ± 0.1 m, body mass: 73 ± 16 kg) walked under two different conditions: control and pain+effusion. Pain+effusion was induced by injection of sterile saline into the joint capsule and hypertonic saline into the infrapatellar fat pad. Sagittal-plane ankle, knee, and hip joint kinematics were recorded and compared following injections using 2×2 mixed model ANOVAs and FANOVAs. The results of ANOVAs detected a condition × time interaction for the peak ankle (F1,18 = 8.56, p = 0.01) and hip joint angle (F1,18 = 5.77, p = 0.03), but did not for the knee joint angle (F1,18 = 0.36, p = 0.56). The functional data analysis, however, found several differences at initial contact (ankle and knee joint), in the mid-stance (each joint) and at toe off (ankle). Although a traditional ANOVA is often appropriate for discrete or summary data, in biomechanical applications, the functional data analysis could be a beneficial alternative. When using the functional data analysis approach, a researcher can (1) evaluate the entire data as a function, and (2) detect the location and magnitude of differences within the evaluated function.

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“…While statistical methods including t-test, ANOVA and covariance analysis were used to analyze and test the coupling or grouping across subjects based on statistic measurements for various applications of kinematic and/or physiological data (Park et al, 2017), advanced techniques of functional data analysis and machine learning demonstrated more promising results in regard to high-dimensional multi-variate gait data (Park et al, 2017). In various health related studies, supervised learning methods, such as SVM, k-nearest neighbors (KNN), linear discriminative analysis (LDA), neural network (NN), were employed for predicting or classifying in between of target and control cohorts, and usually combined with dimensionality reduction approaches, such as principle component analysis (PCA) to discover information from a high-dimensional space (Deluzio & Astephen, 2007;Coffey et al, 2011;Fukuchi et al, 2011;Eskofier et al, 2012;Andrade et al, 2013;Phinyomark et al, 2014;Janidarmian et al, 2015;Derlatka & Bogdan, 2015;Tucker et al, 2015;Watari et al, 2016;Phinyomark et al, 2016;Rida et al, 2016). In addition, some researches were focused on modeling motion dynamics of multivariate kinematic data using hidden Markov model (Mannini & Sabatini, 2012) and Bayesian network (Moon & Pavlović, 2008) for gait pattern recognition.…”

Section: Pattern Recognition and Analysis Of Human Motion Datamentioning

confidence: 99%

Self-expressive subspace clustering to recognize motion dynamics for chronic ankle instability

Qian

Yen

Folmar

et al. 2019

IISE Transactions on Healthcare Systems Engineering

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Ankle sprains and instability are major public health concerns. Up to 70% of individuals do not fully recover from single ankle sprains and eventually develop chronic ankle instability (CAI).The diagnosis of CAI has been mainly based on self-report rather than objective biomechanical measures. The goal of this study is to quantitatively recognize the motion patterns of a multi-joint coordinate system using gait data of bilateral hip, knee, and ankle joints, and further distinguish CAI from control cohorts. We propose an analytic framework, where the concept of subspace clustering is applied to characterize the dynamic gait patterns in a lower dimensional subspace from an interdependent network of multiply joints. A support vector machine model is built to validate the learned measures compared to traditional statistical measures in a leave-one-subject-out cross validation. The experimental results showed >70% classification accuracy on average for the dataset of 47 subjects (24 with CAI and 23 controls) recruited to examine in our designed experiment. It is found that CAI can be observed from other joints (e.g., hips) significantly, which reflects the fact that there exists inter-dependency in the multi-joint coordinate system. The proposed framework presents a potential to support clinical decisions using quantitative measures during diagnosis, treatment, rehabilitation of gait abnormality caused by physical injuries (e.g., ankle sprains in this study) or even central nervous system disorders.

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Functional Data Analyses for the Assessment of Joint Power Profiles During Gait of Stroke Subjects

Cited by 15 publications

References 21 publications

Functional Fatigue Alters Lower-extremity Neuromechanics during a Forward-side Jump

Functional Fatigue Alters Lower-extremity Neuromechanics during a Forward-side Jump

Functional vs. Traditional Analysis in Biomechanical Gait Data: An Alternative Statistical Approach

Self-expressive subspace clustering to recognize motion dynamics for chronic ankle instability

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