Simulated impacts of ankle foot orthoses on muscle demand and recruitment in typically-developing children and children with cerebral palsy and crouch gait

Rosenberg, Michael C.; Steele, Katherine M.

doi:10.1371/journal.pone.0180219

Cited by 26 publications

(24 citation statements)

References 58 publications

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“…While this study focused on estimating muscle architecture for young, healthy individuals using DTI, this framework could further benefit the musculoskeletal modeling field by measuring similar parameters in pathological populations (e.g. individuals with cerebral palsy [54], muscle atrophy [69], muscular dystrophy [70], and the elderly [71]), whose gait and muscle function are often investigated with biomechanical models and simulations [72, 73].…”

Section: Discussionmentioning

confidence: 99%

In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging

2019

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‘Gold standard’ reference sets of human muscle architecture are based on elderly cadaveric specimens, which are unlikely to be representative of a large proportion of the human population. This is important for musculoskeletal modeling, where the muscle force-generating properties of generic models are defined by these data but may not be valid when applied to models of young, healthy individuals. Obtaining individualized muscle architecture data in vivo is difficult, however diffusion tensor magnetic resonance imaging (DTI) has recently emerged as a valid method of achieving this. DTI was used here to provide an architecture data set of 20 lower limb muscles from 10 healthy adults, including muscle fiber lengths, which are important inputs for Hill-type muscle models commonly used in musculoskeletal modeling. Maximum isometric force and muscle fiber lengths were found not to scale with subject anthropometry, suggesting that these factors may be difficult to predict using scaling or optimization algorithms. These data also highlight the high level of anatomical variation that exists between individuals in terms of lower limb muscle architecture, which supports the need of incorporating subject-specific force-generating properties into musculoskeletal models to optimize their accuracy for clinical evaluation.

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Section: Discussionmentioning

confidence: 99%

In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging

2019

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“…This has potential implications for musculoskeletal modelling in a variety of contexts, such as in clinical studies where gait dynamics of pathological populations are investigated using biomechanical simulations (e.g. cerebral palsy (Rosenberg and Steele, 2017)). Such individuals are generally characterised by a reduction in muscle force‐generating capacity relative to younger individuals (D'Souza et al ., 2019), similar to the elderly generic data set used here.…”

Section: Discussionmentioning

confidence: 99%

“…This has potential implications for musculoskeletal modelling in a variety of contexts, such as in clinical studies where gait dynamics of pathological populations are investigated using biomechanical simulations (e.g. cerebral palsy (Rosenberg and Steele, 2017) (Table 1), where the subject-specific models showed the highest coefficients of all the models through all movements tested. So, while the young generic-based models predicted muscle torques in general to a similar degree of accuracy than the subject-specific models when averaged over all subjects, they were not as consistent at doing so between all individuals.…”

Section: F I G U R Ementioning

confidence: 99%

Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models

et al. 2020

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Musculoskeletal modelling is an important platform on which to study the biomechanics of morphological structures in vertebrates and is widely used in clinical, zoological and palaeontological fields. The popularity of this approach stems from the potential to non-invasively quantify biologically important but difficult-to-measure functional parameters. However, while it is known that model predictions are highly sensitive to input values, it is standard practice to build models by combining musculoskeletal data from different sources resulting in 'generic' models for a given species. At present, there are little quantitative data on how merging disparate anatomical

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“…For example, one lower-limb mechanical walking model predicted that an intermediate stiffness in a passive exoskeleton would minimize the energy required to walk, a finding that was later observed experimentally in unimpaired adults [1,14]. More physiologically-detailed musculoskeletal models have been used to predict the impacts of exoskeleton design on muscle activity during walking in children with cerebral palsy and running in unimpaired adults [15,16]. While these studies identified hypothetical relationships between kinematics and the myoelectric impacts of exoskeleton design parameters, their predictions were not evaluated against experimental data.…”

Section: Introductionmentioning

confidence: 99%

Predicting walking response to ankle exoskeletons using data-driven models

Rosenberg

Banjanin

Burden

et al. 2020

Preprint

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6 7 I. KEYWORDS 8 Ankle exoskeleton; data-driven modeling; locomotion; prediction; joint kinematics; muscle activity 9 10 II. ABSTRACT 11Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of 12 exoskeletons on gait remains challenging. We evaluated the ability of three subject-specific phase-varying 13 models to predict kinematic and myoelectric response to ankle exoskeletons during walking, without 14 knowledge of specific user characteristics. Each modela purely phase-varying (PV), linear phase-varying 15 (LPV), and nonlinear phase-varying (NPV) modelleveraged Floquet Theory to predict deviations from a 16 nominal gait cycle due to exoskeleton torque, though the models differed in structure and expected prediction 17 accuracy. For twelve unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted 18 kinematics and muscle activity in response to exoskeleton torque. Prediction accuracy of the LPV model was 19 better than the PV model when predicting less than 12.5% of a stride in the future and explained 55-70% of 20 the variance in hip, knee, and ankle kinematic responses to torque. The LPV model also predicted kinematic 21 responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging 22 to predict across models, explaining at most 10% of the variance in responses. This work highlights the 23 potential of linear phase-varying models to predict complex responses to ankle exoskeletons and inform 24 device design. 25 42 myoelectric responses to the exoskeletons. More physiologically-detailed musculoskeletal models have been 43 used to predict the impacts of exoskeleton design on muscle activity during walking in children with cerebral 44 palsy and running in unimpaired adults [14,15]. While these studies identified hypothetical relationships 45 between kinematics and the myoelectric impacts of exoskeleton design parameters, sensitivity to 46 musculotendon properties likely limited the specificity of their predictions [16,17]. 47 48Challenges to accurately predicting responses to ankle exoskeletons with physics-based models largely stem 49 from uncertainty in adaptation, musculoskeletal physiology, and motor control, which may vary between 50 3 individuals and influence response to exoskeletons. One study found that, while individuals explore different 51 gait patterns to identify an energetically-optimal gait, exploration does not always occur spontaneously, 52 resulting in sub-optimal gait patterns for some users [18]. Popular physiologically-detailed models of human 53 gait typically assume instantaneous and optimal adaptation, which do not reflect how experience and 54 exploration may influence an individual's response to exoskeletons, possibly reducing the accuracy of 55 predicted responses [19,20]. Additionally, when specific measurement sets are unavailable for model 56 parameter tuning, models use population-average based assumptions about musculoskeletal properties and 57 motor control [17,18,[20][21...

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Simulated impacts of ankle foot orthoses on muscle demand and recruitment in typically-developing children and children with cerebral palsy and crouch gait

Cited by 26 publications

References 58 publications

In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging

In vivo human lower limb muscle architecture dataset obtained using diffusion tensor imaging

Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models

Predicting walking response to ankle exoskeletons using data-driven models

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