Biology subjects

Steele, K. M.

Publications and source records attributed to Steele, K. M..

8 recordsLinked to original sources

bioRxiv2024

Human motion data expansion from arbitrary sparsesensors with shallow recurrent decoders

Advances in deep learning and sparse sensing have emerged as powerful tools for monitoring human motion in natural environments. We develop a deep learning architecture, constructed from a shallow recurrent decoder network, that expands human motion data by mapping a limited (sparse) number of sensors to a comprehensive (dense) configuration, thereby inferring the motion of unmonitored body segments. Even with a single sensor, we reconstruct the comprehensive set of time series measurements, which are important for tracking and informing movement-related health and performance outcomes. Notably, this mapping leverages sensor time histories to inform the transformation from sparse to dense sensor configurations. We apply this mapping architecture to a variety of datasets, including controlled movement tasks, gait pattern exploration, and free-moving environments. Additionally, this mapping can be subject-specific (based on an individuals unique data for deployment at home and in the community) or group-based (where data from a large group are used to learn a general movement model and predict outcomes for unknown subjects). By expanding our datasets to unmeasured or unavailable quantities, this work can impact clinical trials, robotic/device control, and human performance by improving the accuracy and availability of digital biomarker estimates.

systems biology↗

bioRxiv2023

A machine learning approach to quantify individual gait responses to ankle exoskeletons

We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individuals response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in nondisabled adults. Discrepancy modeling aims to resolve dynamical inconsistencies between model predictions and real-world measurements. Neural networks identified models of (i) Nominal gait, (ii) Exoskeleton (Exo) gait, and (iii) the Discrepancy (i.e., response) between them. If an Augmented (Nominal+Discrepancy) model captured exoskeleton responses, its predictions should account for comparable amounts of variance in Exo gait data as the Exo model. Discrepancy modeling successfully quantified individuals exoskeleton responses without requiring knowledge about physiological structure or motor control: a model of Nominal gait augmented with a Discrepancy model of response accounted for significantly more variance in Exo gait (median R2 for kinematics (0.928 - 0.963) and electromyography (0.665 - 0.788), (p < 0.042)) than the Nominal model (median R2 for kinematics (0.863 - 0.939) and electromyography (0.516 - 0.664)). However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait (median R2 for kinematics (0.954 - 0.977) and electromyography (0.724 - 0.815)). These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

neuroscience↗

bioRxiv2022

Muscle synergies are flexibly recruited during gait pattern exploration using motor control-based biofeedback

Understanding how the central nervous system coordinates diverse motor outputs has been a topic of extensive investigation. While it is generally accepted that a small set of synergies underlies many common activities, such as walking, whether synergies are equally robust across a broader array of gait patterns or can be flexibly modified remains unclear. Here, we evaluated the extent to which synergies changed as nondisabled adults (n = 14) explored gait patterns using custom biofeedback. Secondarily, we used Bayesian Additive Regression Trees to identify factors which were predictive of synergy modulation. Participants performed 41.1 {+/-} 8.0 gait patterns using biofeedback, during which synergy recruitment changed depending on the type and magnitude of gait pattern modification. Specifically, a consistent set of synergies was recruited to accommodate small deviations from baseline, but additional synergies emerged for larger gait changes. Synergy complexity was similarly modulated; complexity decreased for 82.6% of the attempted gait patterns, however, distal gait mechanics were highly predictive of these changes. In particular, greater ankle dorsiflexion moments and knee flexion through stance, as well as greater knee extension moments at initial contact corresponded to a reduction in synergy complexity. Taken together, these results suggest that the central nervous system preferentially adopts a low-dimensional, largely invariant control strategy, but can modify that strategy to produce diverse gait patterns. Beyond improving understanding of how synergies are recruited during gait, study outcomes may also help identify parameters that can be targeted with interventions to alter synergies and improve motor control following neurological injury.

neuroscience↗

bioRxiv2022

Quantifying template signatures of center-of-mass motion during walking with ankle exoskeletons

Ankle exoskeletons alter whole-body walking mechanics, energetics, and stability by altering center-of-mass (CoM) motion. Controlling the dynamics governing CoM motion is, therefore, critical for maintaining efficient and stable gait. However, how CoM dynamics change with ankle exoskeletons is unknown, and how to optimally model individual-specific CoM dynamics, especially in individuals with neurological injuries, remains a challenge. Here, we evaluated individual-specific changes in CoM dynamics in unimpaired adults and 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 exoskeletons (p>0.16), except for small changes in leg resting length (p<0.001). Conversely, post-stroke paretic-leg rotary stiffness mechanisms increased by 37-50% with zero-stiffness exoskeletons. While unimpaired CoM dynamics appear robust to passive ankle exoskeletons, how neurological injuries alter exoskeleton impacts on CoM dynamics merits further investigation. Our findings support Hybrid-SINDys potential to discover mechanisms describing individual-specific CoM dynamics with assistive devices.

systems biology↗

bioRxiv2021

Number of Synergies Impacts Sensitivity of Gait to Weakness and Contracture

Muscle activity during gait can be described by a small set of synergies, weighted groups of muscles, that are often theorized to reflect underlying neural control. For people with neurologic injuries, like in cerebral palsy or stroke, even fewer (e.g., < 5) synergies are required to explain muscle activity during gait. This reduction in synergies is thought to reflect simplified control strategies and is associated with impairment severity and treatment outcomes. Individuals with neurologic injuries also develop secondary musculoskeletal impairments, like weakness or contracture, that can also impact gait. The combined impacts of simplified control and musculoskeletal impairments on gait remains unclear. In this study, we use a musculoskeletal model constrained to synergies to simulate unimpaired gait. We vary the number of synergies (3-5), while simulating muscle weakness and contracture to examine how altered control impacts sensitivity to muscle weakness and contracture. Our results highlight that reducing the number of synergies increases sensitivity to weakness and contracture. For example, simulations using five-synergy control tolerated 40% and 51% more knee extensor weakness than those using four- and three-synergy control, respectively. Furthermore, the model became increasingly sensitive to contracture and proximal muscle weakness, such as hamstring and hip flexor weakness, when constrained to four- and three-synergy control. However, the models sensitivity to weakness of the plantarflexors and smaller bi-articular muscles was not affected by the number of synergies. These findings provide insight into the interactions between altered control and musculoskeletal impairments, emphasizing the importance of incorporating both in future simulation studies.

bioengineering↗

bioRxiv2020

Predicting walking response to ankle exoskeletons using data-driven models

IIAO_SCPLOWBSTRACTC_SCPLOWDespite 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 models 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 phase-varying models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.

systems biology↗

bioRxiv2019

Children with Cerebral Palsy Return to Baseline Community Arm Movement after Constraint Induced Movement Therapy

ImportanceConstraint Induced Movement Therapy (CIMT) is a common treatment for children with unilateral cerebral palsy (CP). While clinic-based assessments have demonstrated improvements in arm function after CIMT, quantifying if these changes are translated and sustained outside of a clinic setting remains unclear.\n\nObjectiveAccelerometers were used to quantify arm movement for children with CP one week before, during, and 4+ weeks after CIMT and compared to typically-developing (TD) peers.\n\nDesignObservational during CIMT\n\nSettingClinical assessments and treatment occurred in a tertiary hospital and accelerometry data were collected in the community\n\nParticipants7 children with CP (5m/2f, 7.4 {+/-} 1.2 yrs) and 7 TD peers (2m/5f, 7.0 {+/-} 2.3 yrs)\n\nIntervention30-hour CIMT protocol\n\nOutcomes and MeasuresThe use ratio, magnitude ratio, and bilateral magnitude were calculated from the accelerometry data. Clinical measures were evaluated before and after CIMT and surveys were used to assess the feasibility of using accelerometers.\n\nResultsBefore CIMT, children with CP used their paretic arm less than their TD peers. During therapy, their frequency and magnitude of paretic arm use increased in the clinic and in daily life. After therapy, although clinical scores improved, children reverted to baseline accelerometry values. Additionally, children and parents in both cohorts had positive perceptions of wearing accelerometers.\n\nConclusions and RelevanceThe lack of sustained improved accelerometry metrics following CIMT suggest therapy gains did not translate to increased movement outside the clinic. Additional therapy may be needed to help the transfer of skills to the community setting.\n\nWhat this Article AddsThis study compares the movement of children with CP undergoing CIMT in the community setting with their typically developing peers. Additional interventions may be needed in combination with or following CIMT to sustain the benefits of the therapy outside of the clinic.

bioengineering↗

bioRxiv2019

Spasticity reduction in children with cerebral palsy is not associated with reduced energy consumption during walking

BackgroundThe average energy consumption during walking of children with cerebral palsy (CP) is over two times of that of typically developing (TD) children and fatigue is one of the top complaints of children with CP and their families. Spasticity has been theorized to contribute to increased energy consumption during walking in CP, but its role remains unclear.\n\nMethodsWe retrospectively compared the energy consumption of walking in children with diplegic CP before and after selective dorsal rhizotomy (SDR), a surgery that reduces spasticity. A control group of participants with CP who also underwent gait analysis but did not undergo SDR was matched to the SDR group by pre-surgery age, spasticity, and energy consumption. Energy consumption and spasticity were compared at baseline and follow-up for both groups.\n\nFindingsAs expected, the SDR group has a significantly greater decrease (-44%) in spasticity compared to matched peers with CP who did not undergo SDR (-16%, P<0.001). While both groups had a reduction in energy consumption between visits (12 % SDR and 14% no-SDR), there was no difference in the change in energy consumption between groups (P=0.4).\n\nInterpretation: Reducing spasticity did not contribute to greater reductions in energy consumption, suggesting that spasticity has minimal impact on elevated energy consumption during walking for children with CP. Energy consumption and spasticity decrease with age among children with CP. Identifying matched control groups of peers with CP is critical for research involving children with CP to account for changes due to development.\n\nHighlightsO_LIEnergy consumption is not reduced after rhizotomy compared to matched peers\nC_LIO_LISpasticity has minimal contribution to elevated energy during walking\nC_LIO_LIMatched control groups are critical in cerebral palsy research\nC_LI

bioengineering↗