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Posted ContentDOI

Predicting walking response to ankle exoskeletons using data-driven models

25 Aug 2020-bioRxiv (Cold Spring Harbor Laboratory)-
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

Summary (1 min read)

Introduction

  • The authors expected the NPV model’s prediction accuracy to meet or exceed that of the 192 LPV model.
  • The amount of data required to accurately predict response to exoskeletons will restrict the settings in which 252 phase-varying models are practical, such as in clinical gait analysis where datasets typically contain only a 253 few gait cycles [2, 8].
  • To test each model’s generalizability across a range of exoskeleton torque conditions, the authors separately predicted 261 responses to torque in the K1, K2, and K3 datasets, termed held-out conditions, at a 12.5% stride prediction 262 horizon (1/8th of a stride).

VI. DISCUSSION 336

  • The authors evaluated the ability of subject-specific phase-varying models to predict kinematic and myoelectric 337 responses to ankle exoskeleton torques during treadmill walking.
  • 351 the LPV model’s predictions explained more of the variance in kinematic responses to exoskeletons than the 352 PV model, regardless of whether predictions interpolated (K1 and K2) or extrapolated (K3) relative to the 353 training set.

VII. CONCLUSION 459

  • To their knowledge, this is the first study to predict subject-specific responses to ankle exoskeletons using 460 phase-varying models.
  • Without making assumptions about individual physiology or motor control, an LPV 461 model predicted short-time kinematic responses to bilateral passive ankle exoskeletons, though predicting 462 myoelectric responses remains challenging.
  • Results support the utility of LPV models for studying and 463 predicting response to exoskeleton torque.

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1
Predicting walking response to ankle
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exoskeletons using data-driven models
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Michael C. Rosenberg
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, Bora S. Banjanin
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, Samuel A. Burden
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, Katherine M. Steele
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Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
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Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
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Correspondence: mcrosenb@uw.edu
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I. KEYWORDS
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Ankle exoskeleton; data-driven modeling; locomotion; prediction; joint kinematics; muscle activity
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II. ABSTRACT
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Despite recent innovations in exoskeleton design and control, predicting subject-specific impacts of
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exoskeletons on gait remains challenging. We evaluated the ability of three classes of subject-specific phase-
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varying models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without
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requiring prior knowledge of specific user characteristics. Each model phase-varying (PV), linear phase-
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varying (LPV), and nonlinear phase-varying (NPV) leveraged Floquet Theory to predict deviations from a
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nominal gait cycle due to exoskeleton torque, though the models differed in complexity and expected
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prediction accuracy. For twelve unimpaired adults walking with bilateral passive ankle exoskeletons, we
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predicted kinematics and muscle activity in response to three exoskeleton torque conditions. The LPV
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model’s predictions were more accurate than the PV model when predicting less than 12.5% of a stride in
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the future and explained 4970% of the variance in hip, knee, and ankle kinematic responses to torque. The
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LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model.
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Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance
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in responses. This work highlights the potential of data-driven phase-varying models to predict complex
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subject-specific responses to ankle exoskeletons and inform device design and control.
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprintthis version posted September 21, 2020. ; https://doi.org/10.1101/2020.06.18.105163doi: bioRxiv preprint

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III. INTRODUCTION
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Ankle exoskeletons are used to improve kinematics and reduce the energetic demands of locomotion in
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unimpaired adults and individuals with neurologic injuries [1-5]. Customizing exoskeleton properties to
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improve an individual’s gait is challenging and accelerating the iterative experimental process of device
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optimization is an active area of research [6, 7]. Studies examining the effects of exoskeleton properties
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sagittal-plane ankle stiffness or equilibrium ankle angle for passive exoskeletons and torque control laws for
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powered exoskeletons on kinematics, motor control, and energetics have developed design and control
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principles to reduce the energetic demand of walking and improve the quality of gait [1, 6, 8, 9]. Predicting
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how an individual’s gait pattern responds to ankle exoskeletons across stance may inform exoskeleton design
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by enabling rapid evaluation of exoskeleton properties not tested experimentally. Additionally for powered
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exoskeletons, which prescribe torque profiles using feedforward or feedback (e.g. kinematic or myoelectric)
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control laws, predicting responses over even 1020% of a stride may improve tracking performance or
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transitions between control modes [4, 10-12]. However, predicting subject-specific responses to exoskeletons
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remains challenging for unimpaired individuals and those with motor impairments [2, 12, 13].
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Common physics-based models, including simple mechanical models and more physiologically-detailed
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musculoskeletal models, use principles from physics and biology to analyze and predict exoskeleton impacts
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on gait. For example, one lower-limb mechanical walking model predicted that an intermediate stiffness in a
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passive exoskeleton would minimize the energy required to walk, a finding that was later observed
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experimentally in unimpaired adults [1, 14]. More physiologically-detailed musculoskeletal models have
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been used to predict the impacts of exoskeleton design on muscle activity during walking in children with
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cerebral palsy and running in unimpaired adults [15, 16]. While these studies identified hypothetical
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relationships between kinematics and the myoelectric impacts of exoskeleton design parameters, their
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predictions were not evaluated against experimental data.
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprintthis version posted September 21, 2020. ; https://doi.org/10.1101/2020.06.18.105163doi: bioRxiv preprint

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Challenges to accurately predicting responses to ankle exoskeletons with physics-based models largely stem
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from uncertainty in adaptation, musculoskeletal physiology, and motor control, which vary between
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individuals and influence response to exoskeletons. While individuals explore different gait patterns to
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identify an energetically-optimal gait, exploration does not always occur spontaneously, resulting in sub-
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optimal gait patterns for some users [17]. Popular physiologically-detailed models of human gait typically
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assume instantaneous and optimal adaptation, which do not reflect how experience and exploration may
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influence responses to exoskeletons, possibly reducing the accuracy of predicted responses [18, 19].
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Additionally, when specific measurement sets are unavailable for model parameter tuning, population-
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average based assumptions about musculoskeletal properties and motor control are required [17, 20-22].
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However, musculoskeletal properties and motor control are highly uncertain for individuals with motor
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impairments, today’s most ubiquitous ankle exoskeleton users [19, 20, 22, 23]. Musculotendon dynamics and
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motor complexity are known to explain unintuitive exoskeleton impacts on gait energetics, suggesting that
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uncertain musculotendon parameters and motor control may limit the accuracy of predicted changes in gait
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with ankle exoskeletons [19, 21, 24]. Predictions of exoskeleton impacts on gait using physiological models,
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therefore, require accurate estimates of adaptation, musculotendon parameters, and motor control.
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Conversely, data-driven approaches address uncertainty in user-exoskeleton dynamics by representing the
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system entirely from experimental data. For instance, human-in-the-loop optimization provides a model-free
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alternative to physics-based prediction of exoskeleton responses by automatically exploring different
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exoskeleton torque control strategies for an individual [6, 7]. This experimental approach requires no prior
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knowledge about the individual: optimization frameworks identify torque control laws that decrease
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metabolic rate relative to baseline for an individual using only respiratory data and exoskeleton torque
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measurements. However, experimental approaches to exoskeleton optimization require the optimal design to
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be tested, potentially making the search for optimal device parameters time-intensive. Alternatively, machine
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learning algorithms, such as the Random Forest Algorithm, have used retrospective gait analysis and clinical
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprintthis version posted September 21, 2020. ; https://doi.org/10.1101/2020.06.18.105163doi: bioRxiv preprint

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exam data to predict changes in joint kinematics in response to different ankle-foot orthosis designs in
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children with cerebral palsy [8]. This study reported good classification accuracy, though predictions may
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not generalize to new orthosis designs. Unlike physiologically-detailed or physics-based models, human-in-
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the-loop optimization and many machine learning models are challenging to interpret, limiting insight into
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how a specific individual’s physiology influences response to exoskeleton torque. A balance between
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physiologically-detailed and model-free or black-box data-driven approaches may facilitate the prediction
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and analysis of responses to ankle exoskeletons without requiring extensive knowledge of an individual’s
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physiology.
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In this work, we investigated a subject-specific data-driven modeling framework phase-varying models
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that may fill the gap between physiologically-detailed model-based and model-free experimental approaches
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for predicting gait with exoskeletons. Phase-varying models typically have linear structure whose parameters
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are estimated from data, enabling both prediction and analysis of gait with exoskeletons [25, 26]. Unlike
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physiologically-detailed models, phase-varying models do not require knowledge of the physics or control
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of the underlying system. Unlike experimental approaches, the model-based framework enables prediction
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of responses to untested exoskeleton designs or control laws.
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Phase-varying models leverage dynamical properties of stable gaits derived from Floquet Theory, which
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ensures that the convergence of a perturbed trajectory to a stable limit cycle may be locally approximated
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using time-varying linear maps [27]. Similar principles have been shown to generalize to limit cycles in non-
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smooth or hybrid systems, such as human walking [28]. Moreover, phase-varying modeling principles have
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been applied to biological systems, identifying linear phase-varying dynamics to investigate gait stability and
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predict changes in kinematics in response to perturbations [25, 26, 29-31]. Responses to ankle exoskeleton
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torques may be similarly defined as perturbations off an unperturbed (i.e. zero torque) gait cycle, suggesting
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that the principles of phase-varying models will generalize to walking with exoskeletons. To the best of our
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprintthis version posted September 21, 2020. ; https://doi.org/10.1101/2020.06.18.105163doi: bioRxiv preprint

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knowledge, phase-varying models have never been used to study walking with exoskeletons and the extent
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to which the principles underlying phase-varying models of locomotion generalize to walking with
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exoskeletons is unknown.
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To determine if phase-varying models represent useful predictive tools for locomotion with exoskeletons, the
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purpose of this research was to evaluate the ability of subject-specific phase-varying models to predict
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kinematic and myoelectric responses to ankle exoskeleton torque during walking. We predicted responses to
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exoskeletons in unimpaired adults walking with passive ankle exoskeletons under multiple dorsiflexion
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stiffness conditions. We focused on three related classes of phase-varying models with different structures,
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complexity, and expected prediction accuracies: a phase-varying (PV), a linear phase-varying (LPV), and a
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nonlinear phase-varying (NPV) model. Since passive exoskeletons typically elicit small changes in joint
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kinematics and muscle activity, we expected the validity of Floquet Theory for human gait to extend to gait
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with exoskeletons, indicating that the LPV model should accurately predict responses to passive exoskeleton
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torque [1, 25-27, 29]. We, therefore, hypothesized that the LPV models would predict kinematic and
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myoelectric responses to torque more accurately than the PV model and as accurately as the NPV model. To
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exemplify the potential utility of subject-specific phase-varying models in gait analysis with ankle
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exoskeletons, we show how varying the length of model prediction time horizon may inform measurement
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selection for exoskeleton design and control. To assess the viability of data-driven phase-varying models in
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gait analysis settings, we evaluated the effect of limiting the size of the training dataset on prediction
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accuracy.
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IV. METHODS
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A. Experimental protocol
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We collected kinematic and electromyographic (EMG) data from 12 unimpaired adults (6 female / 6 male;
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age = 23.9 ± 1.8 years; height = 1.69 ± 0.10 m; mass = 66.5 ± 11.7 kg) during treadmill walking with bilateral
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(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this preprintthis version posted September 21, 2020. ; https://doi.org/10.1101/2020.06.18.105163doi: bioRxiv preprint

Citations
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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

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

Journal ArticleDOI
TL;DR: In this article , the authors leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults.

1 citations

Posted ContentDOI
01 Jun 2023-bioRxiv
TL;DR: In this article , an equation-free data-driven method for inferring sparse hybrid dynamics from a library of candidate functional forms was proposed to identify optimal sets of physically interpretable mechanisms describing COM dynamics, termed template signatures.
Abstract: Ankle exoskeletons alter whole-body walking mechanics, energetics, and stability. Controlling the dynamics governing center-of-mass (COM) motion is critical for maintaining efficient and stable gait; these dynamics are altered following neurological injury. However, how COM dynamics change with ankle exoskeletons is unknown, and how to optimally model individual-specific COM dynamics is unclear. Here, we evaluated individual-specific changes in COM dynamics in unimpaired adults and a case study of one individual with post-stroke hemiparesis while walking in shoes-only and with zero-stiffness and high-stiffness passive ankle exoskeletons. To identify optimal sets of physically interpretable mechanisms describing COM dynamics, termed template signatures, we leveraged hybrid sparse identification of nonlinear dynamics (Hybrid-SINDy): an equation-free data-driven method for inferring sparse hybrid dynamics from a library of candidate functional forms. In unimpaired adults, Hybrid-SINDy automatically identified spring-loaded inverted pendulum-like template signatures, which did not change with zero- or high-stiffness exoskeletons (p>0.13). Conversely, post-stroke paretic leg and rotary stiffness mechanisms increased by 11% with zero- and high-stiffness exoskeletons, respectively. While unimpaired COM dynamics appear robust to passive ankle exoskeletons, how neurological injuries affect changes in COM dynamics with exoskeletons merits further investigation. Our findings also support Hybrid-SINDy’s potential to discover mechanisms describing individual-specific COM dynamics with assistive devices.

1 citations

References
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TL;DR: The results of this study suggest that an orthosis selection model derived from the RFA can significantly improve outcomes from orthosis use for the diplegic CP population.

26 citations


"Predicting walking response to ankl..." refers background in this paper

  • ...exoskeleton (ankle-foot orthosis) designs in children with cerebral palsy [9]....

    [...]

  • ...exoskeleton design principles to reduce the energetic demand of walking and improve the quality of gait [1, 33 5, 7, 9, 10]....

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TL;DR: This work adapts statistical cross-validation in order to examine whether there are statistically significant asymmetries and, even if so, to test the consequences of assuming bilateral symmetry anyway and shows that ignoring these asymmetry results in a more consistent and predictive model.
Abstract: Many biological phenomena such as locomotion, circadian cycles and breathing are rhythmic in nature and can be modelled as rhythmic dynamical systems. Dynamical systems modelling often involves neglecting certain characteristics of a physical system as a modelling convenience. For example, human locomotion is frequently treated as symmetric about the sagittal plane. In this work, we test this assumption by examining human walking dynamics around the steady state (limit-cycle). Here, we adapt statistical cross-validation in order to examine whether there are statistically significant asymmetries and, even if so, test the consequences of assuming bilateral symmetry anyway. Indeed, we identify significant asymmetries in the dynamics of human walking, but nevertheless show that ignoring these asymmetries results in a more consistent and predictive model. In general, neglecting evident characteristics of a system can be more than a modelling convenience—it can produce a better model.

26 citations


"Predicting walking response to ankl..." refers background or methods in this paper

  • ...spaced equally over the gait cycle, we fit discrete maps between initial and final phases using weighted leastsquares regression [25, 26, 29]....

    [...]

  • ...extension of Floquet Theory to gait with exoskeletons and indicates that, for rhythmic locomotion at a constant speed over level ground, linear phase-varying models appear to have sufficiently complex structure to predict kinematic responses to exoskeletons [25-27, 29]....

    [...]

  • ...and prior models of human locomotion, LPV models appear appropriate for predicting responses to exoskeleton torque over short prediction horizons, evidenced by its similar prediction accuracy to the more complex NPV model and improved prediction accuracy over the less complex PV model [25-27, 29]....

    [...]

  • ...Moreover, phase-varying modeling principles have been applied to biological systems, identifying linear phase-varying dynamics to investigate gait stability and predict changes in kinematics in response to perturbations [25, 26, 29-31]....

    [...]

  • ...Since passive exoskeletons typically elicit small changes in joint kinematics and muscle activity, we expected the validity of Floquet Theory for human gait without exoskeletons to extend to gait with exoskeletons, indicating that the LPV model should accurately predict responses to passive exoskeleton torque [1, 25-27, 29]....

    [...]

Journal ArticleDOI
TL;DR: Experimental evidence of the impact of AFO stiffness and walking speed on joint kinematics and musculotendon function is provided and can provide insight to improve AFO designs and optimize musculoskeletal function for rehabilitation, performance, or other goals.

24 citations


"Predicting walking response to ankl..." refers background in this paper

  • ...However, the myoelectric and kinematic input variables used as model inputs may also be insufficient to encode nonlinear musculotendon dynamics between the initial and final phases, which are impacted by ankle exoskeletons [17, 40, 45]....

    [...]

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


"Predicting walking response to ankl..." refers background in this paper

  • ...More physiologically-detailed musculoskeletal models have 45 been used to predict the impacts of exoskeleton design on muscle activity during walking in children with 46 cerebral palsy and running in unimpaired adults [15, 16]....

    [...]

Proceedings ArticleDOI
01 May 2019
TL;DR: Switched Linear Dynamical Systems (SLDS) models are used to model joint angle kinematic data from healthy individuals walking on a treadmill during normal gait and during gait perturbed by functional electrical stimulation to the ankle muscles and can infer and predict individual-specific responses to FES during swing phase.
Abstract: Modeling individual-specific gait dynamics based on kinematic data could aid development of gait rehabilitation robotics by enabling robots to predict the user’s gait kinematics with and without external inputs, such as mechanical or electrical perturbations. Here we address a current limitation of data-driven gait models, which do not yet predict human gait dynamics nor responses to perturbations. We used Switched Linear Dynamical Systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill during normal gait and during gait perturbed by functional electrical stimulation (FES) to the ankle muscles. Our SLDS models were able to generate joint angle trajectories in each of four gait phases, as well as across an entire gait cycle, given initial conditions and gait phase information. Because the SLDS dynamics matrices encoded significant coupling across joints that differed across indivdiuals, we compared the SLDS predictions to that of a kinematic model, where the joint angles were independent. Joint angle trajectories generated by SLDS and kinematic models were similar over time horizons of a few milliseconds, but SLDS models provided better predictions of gait kinematics over time horizons of up to a second. We also demonstrated that SLDS models can infer and predict individual-specific responses to FES during swing phase. As such, SLDS models may be a promising approach for online estimation and control of and human gait dynamics, allowing robotic control strategies to be tailored to an individual’s specific gait coordination patterns.

3 citations

Frequently Asked Questions (2)
Q1. What are the contributions in "Predicting walking response to ankle exoskeletons using data-driven models" ?

In this paper, the authors predict subject-specific responses to ankle exoskeletons using 460 phase-varying models. 

Improving data-driven models and experimental protocols to study 464 and predict myoelectric responses to exoskeletons represents an important direction for future research. 465 Modeling responses to exoskeletons or other assistive devices using a phase-varying perspective has the 466 potential to inform exoskeleton design for a range of user groups.