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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
More filters
Journal ArticleDOI
TL;DR: If individuals with CP demonstrate reduced complexity of neuromuscular control during gait compared with unimpaired individuals and if changes in control are related to functional ability is sought.
Abstract: Aim Individuals with cerebral palsy (CP) have impaired movement due to a brain injury near birth. Understanding how neuromuscular control is altered in CP can provide insight into pathological movement. We sought to determine if individuals with CP demonstrate reduced complexity of neuromuscular control during gait compared with unimpaired individuals and if changes in control are related to functional ability. Method Muscle synergies during gait were retrospectively analyzed for 633 individuals (age range 3.9–70y): 549 with CP (hemiplegia, n=122; diplegia, n=266; triplegia, n=73; quadriplegia, n=88) and 84 unimpaired individuals. Synergies were calculated using non-negative matrix factorization from surface electromyography collected during previous clinical gait analyses. Synergy complexity during gait was compared with diagnosis subtype, functional ability, and clinical examination measures. Result Fewer synergies were required to describe muscle activity during gait in individuals with CP compared with unimpaired individuals. Changes in synergies were related to functional impairment and clinical examination measures including selective motor control, strength, and spasticity. Interpretation Individuals with CP use a simplified control strategy during gait compared with unimpaired individuals. These results were similar to synergies during walking among adult stroke survivors, suggesting similar neuromuscular control strategies between these clinical populations.

231 citations


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

  • ...59 However, musculoskeletal properties and motor control are highly uncertain for individuals with motor 60 impairments, today’s most ubiquitous ankle exoskeleton users [19, 20, 22, 23]....

    [...]

  • ...Additionally, when specific measurement sets are unavailable for model parameter tuning, population58 average based assumptions about musculoskeletal properties and motor control are required [17, 20-22]....

    [...]

Journal ArticleDOI
TL;DR: Data from this experiment can be used to improve predictive models of human neuromuscular adaptation and guide the design of assistive devices.
Abstract: Techniques proposed for assisting locomotion with exoskeletons have often included a combination of active work input and passive torque support, but the physiological effects of different assistan...

178 citations


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

  • ...remains challenging for unimpaired individuals and those with motor impairments [2, 12, 13]....

    [...]

  • ...kinematic or myoelectric) 36 control laws, predicting responses over even 10–20% of a stride may improve tracking performance or 37 transitions between control modes [4, 10-12]....

    [...]

  • ...distributed between the initial and final phases – to the inputs, resulting in N = 80 inputs [6, 12]....

    [...]

  • ...activity, we found that the average unperturbed gait cycle accounted for only 30-60% of the variance in the 418 K2 data, compared to 60-95% in kinematic signals [1, 9, 12]....

    [...]

Journal ArticleDOI
TL;DR: The active ankle exoskeleton did not simply replace biological ankle function in walking, but rather augmented the total (biological + exoskeletal) ankle moment and power.
Abstract: Ankle exoskeletons can now reduce the metabolic cost of walking in humans without leg disability, but the biomechanical mechanisms that underlie this augmentation are not fully understood. In this study, we analyze the energetics and lower limb mechanics of human study participants walking with and without an active autonomous ankle exoskeleton previously shown to reduce the metabolic cost of walking. We measured the metabolic, kinetic and kinematic effects of wearing a battery powered bilateral ankle exoskeleton. Six participants walked on a level treadmill at 1.4 m/s under three conditions: exoskeleton not worn, exoskeleton worn in a powered-on state, and exoskeleton worn in a powered-off state. Metabolic rates were measured with a portable pulmonary gas exchange unit, body marker positions with a motion capture system, and ground reaction forces with a force-plate instrumented treadmill. Inverse dynamics were then used to estimate ankle, knee and hip torques and mechanical powers. The active ankle exoskeleton provided a mean positive power of 0.105 ± 0.008 W/kg per leg during the push-off region of stance phase. The net metabolic cost of walking with the active exoskeleton (3.28 ± 0.10 W/kg) was an 11 ± 4 % (p = 0.019) reduction compared to the cost of walking without the exoskeleton (3.71 ± 0.14 W/kg). Wearing the ankle exoskeleton significantly reduced the mean positive power of the ankle joint by 0.033 ± 0.006 W/kg (p = 0.007), the knee joint by 0.042 ± 0.015 W/kg (p = 0.020), and the hip joint by 0.034 ± 0.009 W/kg (p = 0.006). This study shows that the ankle exoskeleton does not exclusively reduce positive mechanical power at the ankle joint, but also mitigates positive power at the knee and hip. Furthermore, the active ankle exoskeleton did not simply replace biological ankle function in walking, but rather augmented the total (biological + exoskeletal) ankle moment and power. This study underscores the need for comprehensive models of human-exoskeleton interaction and global optimization methods for the discovery of new control strategies that optimize the physiological impact of leg exoskeletons.

166 citations


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

  • ...Ankle exoskeletons are used to improve kinematics and reduce the energetic demands of locomotion in 27 unimpaired adults and individuals with neurologic injuries [1-5]....

    [...]

  • ...Guided adaptation and extended practice sessions [1, 17] or powered ankle exoskeletons [5, 6] may 410...

    [...]

  • ...147 The marker trajectories were low-pass filtered at 6 Hz using a zero-lag fourth-order Butterworth filter [5]....

    [...]

  • ...These small changes may correspond to larger changes in joint powers or metabolic 350 demands and indicate that the present study is a rigorous test case [1, 2, 5, 24]....

    [...]

Journal ArticleDOI
TL;DR: The findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller, suggesting that robotic assistance from an exoskeletons can allow humans to adopt gait patterns different from their normal choices for locomotion.
Abstract: Robotic ankle exoskeletons can provide assistance to users and reduce metabolic power during walking. Our research group has investigated the use of proportional myoelectric control for controlling robotic ankle exoskeletons. Previously, these controllers have relied on a constant gain to map user’s muscle activity to actuation control signals. A constant gain may act as a constraint on the user, so we designed a controller that dynamically adapts the gain to the user’s myoelectric amplitude. We hypothesized that an adaptive gain proportional myoelectric controller would reduce metabolic energy expenditure compared to walking with the ankle exoskeleton unpowered because users could choose their preferred control gain. We tested eight healthy subjects walking with the adaptive gain proportional myoelectric controller with bilateral ankle exoskeletons. The adaptive gain was updated each stride such that on average the user’s peak muscle activity was mapped to maximal power output of the exoskeleton. All subjects participated in three identical training sessions where they walked on a treadmill for 50 minutes (30 minutes of which the exoskeleton was powered) at 1.2 ms-1. We calculated and analyzed metabolic energy consumption, muscle recruitment, inverse kinematics, inverse dynamics, and exoskeleton mechanics. Using our controller, subjects achieved a metabolic reduction similar to that seen in previous work in about a third of the training time. The resulting controller gain was lower than that seen in previous work (β=1.50±0.14 versus a constant β=2). The adapted gain allowed users more total ankle joint power than that of unassisted walking, increasing ankle power in exchange for a decrease in hip power. Our findings indicate that humans prefer to walk with greater ankle mechanical power output than their unassisted gait when provided with an ankle exoskeleton using an adaptive controller. This suggests that robotic assistance from an exoskeleton can allow humans to adopt gait patterns different from their normal choices for locomotion. In our specific experiment, subjects increased ankle power and decreased hip power to walk with a reduction in metabolic cost. Future exoskeleton devices that rely on proportional myolectric control are likely to demonstrate improved performance by including an adaptive gain.

136 citations


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

  • ...kinematic or myoelectric) 36 control laws, predicting responses over even 10–20% of a stride may improve tracking performance or 37 transitions between control modes [4, 10-12]....

    [...]

  • ...The LPV and NPV models’ accurate predictions over short prediction horizons make 407 them primarily useful for exoskeleton control [10, 11]....

    [...]

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.