JournalArticle

KM Steele, MH Schwartz (2022) “Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach”

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Journal Article in Frontiers in Human Neuroscience

Altered motor control is common in cerebral palsy (CP). Understanding how altered motor control affects movement and treatment outcomes is important but challenging due to complex interactions with other neuromuscular impairments. While regression can be used to examine associations between impairments and movement, causal modeling provides a mathematical framework to specify assumed causal relationships, identify covariates that may introduce bias, and test model plausibility.

FIGURE 1 Directed Acyclic Graph (DAG) describing the assumed causal relationships between SEMLS (exposure) and 1GDI (outcome). The causal relationship between SEMLS and 1GDI is mediated by changes in impairments (1Imp). Baseline GDI (GDIpre) and 1GDI are related by measurement methods and other, unmeasured factors. Baseline impairment (Imppre), surgical history (Hx), and Age are also included as causal factors. The DAG also includes unmeasured factors related to general CP severity, which impact baseline impairment and surgical history. The step-by-step process and rationale for this DAG are available in the Supplementary Material and an interactive version is available on dagitty (http://dagitty.net/mUCSPWo).Aim: The goal of this research was to quantify the causal effects of altered motor control and other impairments on gait, before and after single-event multi-level orthopedic surgery (SEMLS).

Methods: We evaluated the impact of SEMLS on change in Gait Deviation Index (ΔGDI) between gait analyses. We constructed our causal model with a Directed Acyclic Graph that included the assumed causal relationships between SEMLS, ΔGDI, baseline GDI (GDIpre), baseline neurologic and orthopedic impairments (Imppre), age, and surgical history. We identified the adjustment set to evaluate the causal effect of SEMLS on ΔGDI and the impact of Imppre on ΔGDI and GDIpre. We used Bayesian Additive Regression Trees (BART) and accumulated local effects to assess relative effects.

Results: We prospectively recruited a cohort of children with bilateral CP undergoing SEMLS (N = 55, 35 males, age: 10.5 ± 3.1 years) and identified a control cohort with bilateral CP who did not undergo SEMLS (N = 55, 30 males, age: 10.0 ± 3.4 years). There was a small positive causal effect of SEMLS on ΔGDI (1.70 GDI points). Altered motor control (i.e., dynamic and static motor control) and strength had strong effects on GDIpre, but minimal effects on ΔGDI. Spasticity and orthopedic impairments had minimal effects on GDIpre or ΔGDI.

Interpretation: Altered motor control did have a strong effect on GDIpre, indicating that these impairments do have a causal effect on a child’s gait pattern, but minimal effect on expected changes in GDI after SEMLS. Heterogeneity in outcomes suggests there are other factors contributing to changes in gait. Identifying these factors and employing causal methods to examine the complex relationships between impairments and movement will be required to advance our understanding and care of children with CP.

EC Kuska, N Mehrabi, MH Schwartz, KM Steele (2022) “Number of synergies impacts sensitivity of gait to weakness and contracture”

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Journal Article in Journal of Biomechanics

Muscle activity during gait can be described by a small set of synergies, weighted groups of muscles, that are theorized to reflect underlying neural control. For people with neurologic injuries, like cerebral palsy or stroke, even fewer synergies are required to explain muscle activity during gait. This reduction in synergies is thought to reflect altered control and is associated with impairment severity and treatment outcomes. Individuals with neurologic injuries also develop secondary musculoskeletal impairments, like weakness or contracture, that can impact gait. Yet, the combined impacts of altered control and musculoskeletal impairments on gait remains unclear.

A two-dimensional sagittal plane musculoskeletal model and synergy simulation framework tracked unimpaired gait kinematics. The model had nine degrees of freedom, including right and left leg hip, knee, and ankle flexion, actuated by eight muscles per leg. Fixed sets of synergies constrained control, forcing the direct collocation algorithm to solve for synergy activations. The objective function minimized deviations from unimpaired kinematics and the sum of muscle activations squared (neural demand). Weakness, simulated by a reduction in maximum isometric force, and contracture, simulated by a reduction in tendon slack length, were progressively increased for each muscle or muscle group until the simulation failed to replicate unimpaired gait. Kinematic deviations and convergence determined the success of the simulation. The primary outcomes were (1) musculoskeletal impairment thresholds, defined by the amount of weakness or contracture before failure, and (2) neural demand of each gait cycle.Aim: In this study, we use a two-dimensional musculoskeletal model constrained to synergy control to simulate unimpaired gait.

Methods: We vary the number of synergies, while simulating muscle weakness and contracture to examine how altered control impacts sensitivity to musculoskeletal impairment while tracking unimpaired gait.

Results: Results demonstrate that reducing the number of synergies increases sensitivity to weakness and contracture for specific muscle groups. For example, simulations using five-synergy control tolerated 40% and 51% more knee extensor weakness than those using four- or three-synergy control, respectively. Furthermore, when constrained to four- or three-synergy control, the model was increasingly sensitive to contracture and weakness of proximal muscles, such as the hamstring and hip flexors. Contrastingly, neither the amount of generalized nor plantarflexor weakness tolerated was affected by the number of synergies.

Interpretation: These findings highlight the interactions between altered control and musculoskeletal impairments, emphasizing the importance of measuring and incorporating both in future simulation and experimental studies.

HA Feldner, C Papazian, KM Peters, CJ Cruetzfeldt, KM Steele (2021) “Clinical Use of Surface Electromyography to Track Acute Upper Extremity Muscle Recovery after Stroke: A Descriptive Case Study of a Single Patient”

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Journal Article in Applied System Innovation:

This work highlights the potential of wearable technologoies to monitor muscle activity changes during stroke recovery in acute clinical settings and their importance for motivation and understanding of progression from the survivor’s point of view: ‘I was hopeful that it would show signs of things that are occurring when I couldn’t physically feel it…if you had other scientific evidence that things were happening, even beyond their notion that it would, it gives you a lot of hope. You just have to be patient, and it’s harder to take when someone tells you, but easier to understand if someone actually shows you’.

Left image depicts arm with pads placed over muscle with right pictures depicting similar image

Aim: Describe the use of wireless sEMG sensors to examine changes in muscle activity during acute and subacute phases of stroke recovery, and understand the participant’s perceptions of sEMG monitoring.

Method: Muscle activity was tracked by five wireless sEMG sensors beginning three days post-stroke and continued through discharge from inpatient rehabilitation. Activity logs were completed each session, and a semi-structured interview occurred at the final session with three- and eight-month follow-up sessions.

Results: The longitudinal monitoring of muscle and movement recovery in the clinic and community was feasible using sEMG sensors. The participant and medical team felt monitoring was unobtrusive, interesting, and motivating for recovery, but desired greater in-session feedback to inform rehabilitation.

Interpretation: This work highlights that barriers in equipment and signal quality still exist, but capitalizing on wearable sensing technology in the clinic holds promise for enabling personalized stroke recovery.

MC Rosenberg, BS Banjanin, SA Burden, KM Steele (2020) “Predicting walking response to ankle exoskeleton using data driven models”

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Journal Article in The Royal Society:

This work highlights the potential of data-driven models grounded in dynamical systems theory to predict complex individualized responses to ankle exoskeletons., without requiring explicit knowledge of the individual’s physiology or motor control

silhouette walking on left with purple lines and projections on right elipsoids and colored spheres

Aim: Evaluate the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics.

Method: Data from 12 unimpaired adults walking with bilateral passive ankle exoskeletons were captured. PV, linear PV (LPV), and nonlinear PV (NPV) models leveraged Floquet theory to kinematics and muscle activity in response to three exoskeleton torque conditions.

Results: The LPV model’s predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49–70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses.

Interpretation: This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.

M Yamagami, KM Steele, SA Burden (2020) “Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance”

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Journal Article in ACM Conference on Human Factors in Computing Systems (CHI) 2020 Preceedings:

These results suggest that control theory modeling can provide a platform to successfully quantify device performance in the absence of errors arising from motor impairments

Split image of upper body of user holding rod and slider with computer screen

Photo (top and bottom) of a user using a slider (top) and muscles (bottom) to control a cursor on the screen.
(Top image) Side image of user. User rests their elbow and pinches the slider and moves the slider towards and away from their body to control the cursor.
(Bottom image) Side image of user. User is strapped to a rigid device holding a bar with hands supinated towards the ceiling, with the forearms at a 90 degree angle from the upper arms.
Electrodes are placed on the biceps and triceps and labelled. Arrows pointing up and down indicate that users move their arm up and down to control the cursor.

 

Background: Manual device interaction requires precise coordination which may be difficult for users with motor impairments. Muscle interfaces provide alternative interaction methods that may enhance performance, but have not yet been evaluated for simple (eg. mouse tracking) and complex (eg. driving) continuous tasks. Control theory enables us to probe continuous task performance by separating user input into intent and error correction to quantify how motor impairments impact device interaction

Aim:  Propose and extend an experimental and analytical method to guide future development of accessible interfaces like muscle interfaces using control theory

Method: We compared the effectiveness of a manual versus a muscle interface for eleven users without and three users with motor impairments performing continuous tasks.

Results: Both user groups preferred and performed better with the muscle versus the manual interface for the complex continuous task.

Interpretation: Results suggest muscle interfaces and algorithms that can detect and augment user intent may be especially useful for future design of interfaces for continuous tasks.

 

Momona also gave a phenomenal talk on this paper last week in the University of Washington’s ‘DUB Shorts’ series (video posted below). Nice job Momona!