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Michael C. Rosenberg

Bio: Michael C. Rosenberg is an academic researcher from University of Washington. The author has contributed to research in topics: Gait & Exoskeleton. The author has an hindex of 1, co-authored 3 publications receiving 11 citations.
Topics: Gait, Exoskeleton, Ankle, Medicine, Biology

Papers
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Journal ArticleDOI
13 Jul 2017-PLOS ONE
TL;DR: Findings support the potential use of powered AFOs for children with crouch gait, and highlight how subject-specific kinematics and kinetics may influence muscle demand and recruitment to inform AFO design.
Abstract: Passive ankle foot orthoses (AFOs) are often prescribed for children with cerebral palsy (CP) to assist locomotion, but predicting how specific device designs will impact energetic demand during gait remains challenging. Powered AFOs have been shown to reduce energy costs of walking in unimpaired adults more than passive AFOs, but have not been tested in children with CP. The goal of this study was to investigate the potential impact of powered and passive AFOs on muscle demand and recruitment in children with CP and crouch gait. We simulated gait for nine children with crouch gait and three typically-developing children with powered and passive AFOs. For each AFO design, we computed reductions in muscle demand compared to unassisted gait. Powered AFOs reduced muscle demand 15-44% compared to unassisted walking, 1-14% more than passive AFOs. A slower walking speed was associated with smaller reductions in absolute muscle demand for all AFOs (r2 = 0.60-0.70). However, reductions in muscle demand were only moderately correlated with crouch severity (r2 = 0.40-0.43). The ankle plantarflexor muscles were most heavily impacted by the AFOs, with gastrocnemius recruitment decreasing 13-73% and correlating with increasing knee flexor moments (r2 = 0.29-0.91). These findings support the potential use of powered AFOs for children with crouch gait, and highlight how subject-specific kinematics and kinetics may influence muscle demand and recruitment to inform AFO design.

18 citations

Posted ContentDOI
25 Aug 2020-bioRxiv
TL;DR: The ability of subject-specific phase-varying models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics, is evaluated.
Abstract: II Abstract Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging We evaluated the ability of three classes of subject-specific phase-varying models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics Each model – phase-varying (PV), linear phase-varying (LPV), and nonlinear phase-varying (NPV) – leveraged Floquet Theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy For twelve unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions The LPV model’s predictions were more accurate than the PV model when predicting less than 125% of a stride in the future and explained 49–70% of the variance in hip, knee, and ankle kinematic responses to torque The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses This work highlights the potential of data-driven phase-varying models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control

8 citations

Journal ArticleDOI
TL;DR: The ability of three classes of subject-specific phase-varying PV models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics is evaluated.
Abstract: Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics. Each model-PV, linear PV (LPV) and nonlinear PV (NPV)-leveraged Floquet theory to predict deviations from a nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected prediction accuracy. For 12 unimpaired adults walking with bilateral passive ankle exoskeletons, we predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV model's predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49-70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses. This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.

7 citations

Posted ContentDOI
23 Dec 2022-bioRxiv
TL;DR: In this paper , the authors developed a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of Gait dynamics.
Abstract: Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics – i.e., gait signatures – for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.

3 citations

Posted ContentDOI
01 Feb 2022
TL;DR: Agreement between automatically-identified template signatures and those found from decades of biomechanics research supports Hybrid-SINDy’s potential to accelerate discovery of mechanisms underlying impaired locomotion and assistive device responses.
Abstract: Predicting ankle exoskeleton impacts on an individual’s walking function, stability, and efficiency remains challenging. Characterizing how the dynamics underlying center-of-mass (COM) mechanics and energetics change with exoskeletons may improve predictions of exoskeleton responses. We evaluated changes in individual-specific COM dynamics in unimpaired adults and one individual with post-stroke hemiparesis while walking in shoes-only and with passive ankle exoskeletons. We leveraged hybrid sparse identification of nonlinear dynamics (Hybrid-SINDy) – an equation-free data-driven method for inferring nonlinear hybrid dynamics using a large library of candidate functional forms – to identify functional forms that best modelled physical mechanisms describing leg-specific COM dynamics, termed template signatures. Across participants, Hybrid-SINDy identified template signatures comprised of leg stiffness and resting length, similar to common spring-loaded inverted pendulum models. Rotary stiffness mechanisms were identified in only 40-50% of participants. Unimpaired template signatures did not change with ankle exoskeletons (p > 0.13). Conversely, post-stroke paretic leg and rotary stiffness increased by 11% with zero- and high-stiffness exoskeleton, respectively, suggesting that COM dynamics may be more sensitive to exoskeletons following neurological injury. Agreement between our automatically-identified template signatures and those found from decades of biomechanics research supports Hybrid-SINDy’s potential to accelerate the discovery of mechanisms underlying impaired locomotion and assistive device responses.

2 citations


Cited by
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Journal ArticleDOI
14 Aug 1987-JAMA
TL;DR: Although a variety of univariate statistics are included, certain topics that are important in medical research are not, and there is little or no discussion of multiple regression, life-table techniques, or pooling of studies.
Abstract: This book attempts to achieve a difficult goal: to teach statistics to the novice so as to impart a liking and understanding of statistics. The book is geared toward a medical audience, since most examples are from the medical literature. The structure of the book consists of the following elements in each chapter: a small number of statistical rules of thumb, followed by a nontechnical explanation, a demonstration of how to work through the mechanics of doing the statistical test in question, a summary, and sample problems to be solved by the reader. (The answers, with explanations, are provided in an appendix.) Although a variety of univariate statistics are included, certain topics that are important in medical research are not. For example, there is little or no discussion of multiple regression, life-table techniques, or pooling of studies. These omissions, especially of multiple regression, are unfortunate. The Primer was derived from

898 citations

Journal ArticleDOI
TL;DR: This review article classifies recent predictive simulation methods for human gait analysis according to three categories: the human models used, problem formulation, and simulation solvers.
Abstract: Human gait analysis is a complex problem in biomechanics because of highly nonlinear human motion equations, muscle dynamics, and foot-ground contact. Despite a large number of studies in human gait analysis, predictive human gait simulation is still challenging researchers to increase the accuracy and computational efficiency for evaluative studies (e.g., model-based assistive device controllers, surgical intervention planning, athletic training, and prosthesis and orthosis design). To assist researchers in this area, this review article classifies recent predictive simulation methods for human gait analysis according to three categories: (1) the human models used (i.e., skeletal, musculoskeletal and neuromusculoskeletal models), (2) problem formulation, and (3) simulation solvers. Human dynamic models are classified based on whether muscle activation and/or contraction dynamics or joint torques (instead of muscle dynamics) are employed in the analysis. Different formulations use integration and/or differentiation or implicit-declaration of the dynamic equations. A variety of simulation solvers (i.e., semi- and fully-predictive simulation methods) are studied. Finally, the pros and cons of the different formulations and simulation solvers are discussed.

42 citations

Journal ArticleDOI
15 Oct 2019-PLOS ONE
TL;DR: These data 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.
Abstract: ‘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.

35 citations

Journal ArticleDOI
TL;DR: In this article , a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed was designed to reduce metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s −1 .
Abstract: Abstract Personalized exoskeleton assistance provides users with the largest improvements in walking speed 1 and energy economy 2–4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s −1 . Human movements encode information that can be used to personalize assistive devices and enhance performance.

29 citations

Journal ArticleDOI
TL;DR: In this article, the relative accuracy of subject-specific muscle architecture from various MRI sequences for predicting muscle forces in musculoskeletal models is tested against those derived from generic, averaged data sets.
Abstract: This dataset contains input and output data for the following journal article, where the relative accuracy of subject-specific muscle architecture from various MRI sequences for predicting muscle forces in musculoskeletal models is tested against those derived from generic, averaged data sets Charles, JP, Grant, B, D' Aout, K, Bates, KT (2020) Subject-specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models J Anat Please refer to READMEtxt for more information

16 citations