Age-Related Differences in Gait Kinematics, Kinetics, and Muscle Function: A Principal Component Analysis

Schloemer, Sarah; Thompson, Jeffrey R.; Silder, Amy; Thelen, Darryl G.; Siston, Robert A.

doi:10.1007/s10439-016-1713-4

Cited by 37 publications

(24 citation statements)

References 30 publications

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“…Moreover, algorithms for mobility assessment should be validated separately for each type of neurological movement disorder, as they may be associated with distinct movements patterns. 25,62 Even healthy people move differently in different ages 63,64 and with different fitness levels. 65 Special emphasis must be placed on a more sophisticated analysis of unsupervised data.…”

Section: How To Implement In Clinical Routine and Future Research?mentioning

confidence: 99%

Long-term unsupervised mobility assessment in movement disorders

Warmerdam

Hausdorff

Atrsaei

et al. 2020

The Lancet Neurology

231

214

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Digital health technologies that quantify mobility in unsupervised, daily-living environments are emerging as a complementary evaluation approach in neurology. Data collected in these ecologically valid, patient-relevant settings can overcome significant limitations of conventional clinical assessments. Unsupervised assessments can capture fluctuating and rare events and have the promise of supporting clinical decision-making and serving as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (i.e. in the lab or clinical) settings point to large disparities, even in the same parameters of mobility (up to 180% difference). These differences appear to be influenced by psychological, physiological, cognitive, environmental, and technical factors and by the specific aspect of mobility and diagnosis. To facilitate the successful adaptation of the unsupervised assessment of mobility in the clinic and in clinical trials, clinicians and future work should take into account these disparities and the multiple factors that contribute to them.

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Section: How To Implement In Clinical Routine and Future Research?mentioning

confidence: 99%

Long-term unsupervised mobility assessment in movement disorders

Warmerdam

Hausdorff

Atrsaei

et al. 2020

The Lancet Neurology

231

214

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“…Many of these variables are not commonly determined in human experiments because of the significant pain and discomfort to the subject with approaches such as fine-wire electromyography (EMG) and tendon buckle transducers. Open-Sim 1 is an open-source musculoskeletal modeling and simulation software employed by over 50,000 unique users 2 to investigate dynamic movements such as walking, [3][4][5] running, 6,7 rising from a chair, 8 and climbing stairs. 9 OpenSim allows users several choices for developing simulations of movement.…”

mentioning

confidence: 99%

Effects of Optimization Technique on Simulated Muscle Activations and Forces

Roelker

Caruthers

Hall

et al. 2020

Journal of Applied Biomechanics

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Two optimization techniques, static optimization (SO) and computed muscle control (CMC), are often used in OpenSim to estimate the muscle activations and forces responsible for movement. Although differences between SO and CMC muscle function have been reported, the accuracy of each technique and the combined effect of optimization and model choice on simulated muscle function is unclear. The purpose of this study was to quantitatively compare the SO and CMC estimates of muscle activations and forces during gait with the experimental data in the Gait2392 and Full Body Running models. In OpenSim (version 3.1), muscle function during gait was estimated using SO and CMC in 6 subjects in each model and validated against experimental muscle activations and joint torques. Experimental and simulated activation agreement was sensitive to optimization technique for the soleus and tibialis anterior. Knee extension torque error was greater with CMC than SO. Muscle forces, activations, and co-contraction indices tended to be higher with CMC and more sensitive to model choice. CMC’s inclusion of passive muscle forces, muscle activation-contraction dynamics, and a proportional-derivative controller to track kinematics contributes to these differences. Model and optimization technique choices should be validated using experimental activations collected simultaneously with the data used to generate the simulation.

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“…The transition mode is determined by the vertical push-off: a push-off inferior to one body weight will result in a passive transition mode while a push-off superior to one body weight will result in an active transition mode [29]. As a matter of fact, the major determinants of the transition mode are the plantar flexors of the back leg since they are the greatest contributors to the vertical push-off [30]. A reduction of the plantar flexion in elderly adults is commonly observed in the literature [31] and is attributed to an underutilisation of the available muscular capacity [32] and/or a failure in the interaction between brain, interneurons, motoneurons, and sensory feedback [33].…”

Section: Discussionmentioning

confidence: 99%