Simultaneously assessing amplitude and temporal effects in biomechanical trajectories using nonlinear registration and statistical nonparametric mapping

Pataky, Todd C.; Robinson, Mark A.; Vanrenterghem, Jos; Donnelly, Cyril J.

doi:10.1016/j.jbiomech.2022.111049

Cited by 15 publications

(11 citation statements)

References 27 publications

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“…Specifically, we found that the principal modes of variation in our dataset of unimpaired, paretic and non-paretic gait corresponded to statistically significant differences across the three categories, in both spatial and temporal domains. These results are consistent with recent work that employed a similar approach to identify statistically significant amplitude and phase differences between the centre-of-pressure trajectories of unimpaired individuals and patellofemoral pain syndrome patients [26]. Notably, the gait characteristics corresponding to regions of high spatiotemporal variance in our data were similar to manually selected features and point metrics from prior clinical studies.…”

Section: Discussionsupporting

confidence: 90%

“…To mitigate these issues, we look into elastic functional data analysis (FDA), a recent statistical framework under which to perform alignment, termed elastic alignment, and calculate representative templates of temporally variable data [23–25]. Promising recent work has shown that elastic FDA can enhance statistical analysis of impaired gait, but has yet to be extended for automating extraction of biomechanically interpretable features from gait [26]. In this work, we build on existing literature by integrating ideas from continuous gait analysis, time series alignment and spatiotemporal functional analysis.…”

Section: Introductionmentioning

confidence: 99%

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A continuous statistical-geometric framework for normative and impaired gaits

Swaminathan

Tolkova

Baker

et al. 2022

J. R. Soc. Interface.

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A quantitative analysis of human gait patterns in space–time provides an opportunity to observe variability within and across individuals of varying motor capabilities. Impaired gait significantly affects independence and quality of life, and thus a large part of clinical research is dedicated to improving gait through rehabilitative therapies. Evaluation of these paradigms relies on understanding the characteristic differences in the kinematics and underlying biomechanics of impaired and unimpaired locomotion, which has motivated quantitative measurement and analysis of the gait cycle. Previous analysis has largely been limited to a statistical comparison of manually selected pointwise metrics identified through expert knowledge. Here, we use a recent statistical-geometric framework, elastic functional data analysis (FDA), to decompose kinematic data into continuous ‘amplitude’ (spatial) and ‘phase’ (temporal) components, which can then be integrated with established dimensionality reduction techniques. We demonstrate the utility of elastic FDA through two unsupervised applications to post-stroke gait datasets. First, we distinguish between unimpaired, paretic and non-paretic gait presentations. Then, we use FDA to reveal robust, interpretable groups of differential response to exosuit assistance. The proposed methods aim to benefit clinical practice for post-stroke gait rehabilitation, and more broadly, to automate the quantitative analysis of motion.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

A continuous statistical-geometric framework for normative and impaired gaits

Swaminathan

Tolkova

Baker

et al. 2022

J. R. Soc. Interface.

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“…Each method has pros and cons. For example, the SPM avoids summary metric extraction (Pataky et al, 2016a), but cannot assure single point/feature comparison and cannot avoid the normalization (i.e., time or angle) (Pataky et al, 2022). In contrast, scalar analysis (e.g., interval averaging or peak values) allows us to compare single-point values such as peak, minimum, and median (Degrave et al, 2020) but cannot avoid continuum summary metric extraction (Pataky et al, 2016a).…”

Section: Discussionmentioning

confidence: 99%

Application of statistical parametric mapping for comparison of scapular kinematics and EMG

Kobayashi

Umehara

Pataky

et al. 2022

Journal of Biomechanics

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“…Consequently, critical insights into the unique joint motion characteristics of individuals with ACL injuries, such as variability in motion during specific time intervals and the interplay between different conditions, might not have been fully captured. To overcome these limitations, future research should include analytical techniques capable of identifying variability across specific periods, such as Statistical Parametric Mapping [ 43 ], to provide a more comprehensive understanding of the factors contributing to ACL injury risk.…”

Section: Discussionmentioning

confidence: 99%

Dependence on visual information in patients with ACL injury for multi-joint coordination during single-leg squats: a case control study

Toriyama,

Nakamae,

Abe

et al. 2024

BMC Sports Sci Med Rehabil

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Background The influence of vision on multi-joint control during dynamic tasks in anterior cruciate ligament (ACL) deficient patients is unknown. Thus, the purpose of this study was to establish a new method for quantifying neuromuscular control by focusing on the variability of multi-joint movement under conditions with different visual information and to determine the cutoff for potential biomarkers of injury risk in ACL deficient individuals. Methods Twenty-three ACL deficient patients and 23 healthy subjects participated in this study. They performed single-leg squats under two different conditions: open eyes (OE) and closed eyes (CE). Multi-joint coordination was calculated with the coupling angle of hip flexion, hip abduction and knee flexion. Non-linear analyses were performed on the coupling angle. Dependence on vision was compared between groups by calculating the CE/OE index for each variable. Cutoff values were calculated using ROC curves with ACL injury as the dependent variable and significant variables as independent variables. Results The sample entropy of the coupling angle was increased in all groups under the CE condition (P < 0.001). The CE/OE index of coupling angle variability during the descending phase was higher in ACL deficient limbs than in the limbs of healthy participants (P = 0.036). The CE/OE index of sample entropy was higher in the uninjured limbs of ACL deficient patients than in the limbs of healthy participants (P = 0.027). The cutoff value of the CE/OE index of sample entropy was calculated to be 1.477 (Sensitivity 0.957, specificity 0.478). Conclusion ACL deficient patients depended on vision to control multiple joint movements not only on the ACL deficient side but also on the uninjured side during single leg squat task. These findings underscore the importance of considering visual dependence in the assessment and rehabilitation of neuromuscular control in ACL deficient individuals.

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Simultaneously assessing amplitude and temporal effects in biomechanical trajectories using nonlinear registration and statistical nonparametric mapping

Cited by 15 publications

References 27 publications

A continuous statistical-geometric framework for normative and impaired gaits

A continuous statistical-geometric framework for normative and impaired gaits

Application of statistical parametric mapping for comparison of scapular kinematics and EMG

Dependence on visual information in patients with ACL injury for multi-joint coordination during single-leg squats: a case control study

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