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Recent advancements in computer vision and machine learning enable autonomous measurement of total knee arthroplasty kinematics through single-plane fluoroscopy. However, symmetric components present challenges in optimization routines, causing "symmetry traps" and ambiguous poses. Achieving clinically robust kinematics measurement requires addressing this issue. We devised an algorithm that converts a "true" pose to its corresponding "symmetry trap" orientation. From a dataset of nearly 13,000 human supervised kinematics, this algorithm constructs an augmented dataset of "true" and "symmetry trap" kinematics, used to train eight classification machine learning algorithms. The outputs from the highest-performing algorithm classify kinematics sequences as 'obviously true' or 'potentially ambiguous.' We construct a spline through 'obviously true' poses, and 'ambiguous' poses are compared to the spline to determine correct orientation. The machine learning algorithms achieved 88-94% accuracy on our internal test set and 91-93% on our external test set. Applying our spline algorithm to kinematics sequences yielded 91.1% accuracy, 94% specificity, but 67% sensitivity. The accuracy of standard ML algorithms for implants within 5 degrees of a pure-lateral view was 71%, rising to 88% beyond 5 degrees. This pioneering study systematizes addressing model-image registration issues with symmetric tibial implants. High accuracy suggests potential use of ML algorithms to mitigate shape-ambiguity errors in pose measurements from single-plane fluoroscopy. Our results also suggest an imaging protocol for measuring kinematics that favors more oblique viewing angles, which could further disambiguate "true" and "symmetry trap" poses., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: People with knee osteoarthritis walk with excessive muscle co-contraction that can accelerate disease progression. Central pain sensitization is common in people with knee osteoarthritis and may be related to walking patterns. The objective of this study was to examine the relation of central pain sensitization with muscle co-contraction during walking in people with knee osteoarthritis., Methods: This study reports secondary analysis from baseline data of two clinical trials (n = 90 participants with knee osteoarthritis). The presence of central pain sensitization was measured by mechanical temporal summation at the patella and the wrist. Quadriceps and hamstrings activation was assessed using surface electromyography during walking at self-selected and fast paces. Muscle co-contraction indices for vastus medialis-medial hamstrings and vastus lateralis-lateral hamstrings muscle pairs were calculated during stance phases. Co-contraction outcomes were compared between people with and without mechanical temporal summation at each site, adjusting for age, sex, and body mass index., Findings: People with mechanical temporal summation at the knee had greater vastus lateralis-lateral hamstrings co-contraction while walking at a fast pace (P = 0.04). None of the other differences was statistically significant, but the overall trends and effect sizes indicated greater co-contraction in people with temporal summation at the knee irrespective of gait phase, walking speed, or muscle pairs., Interpretation: Central pain sensitization, assessed as mechanical temporal summation at the knee, is related to greater knee muscle co-contraction during fast walking in people with knee osteoarthritis. Thus, mitigating central sensitization may be an interventional target to reduce muscle co-contraction for people with knee osteoarthritis., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Benjamin Senderling reports financial support was provided by Boston University. Tuhina Neogi reports financial support was provided by National Institutes of Health. Tuhina Neogi reports a relationship with Pfizer that includes: consulting or advisory and funding grants. Deepak Kumar reports financial support was provided by National Institutes of Health. Deepak Kumar reports financial support was provided by Pfizer., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Varus thrust during walking, visualized as excessive frontal plane knee motion during weight acceptance, is a modifiable risk factor for progression of knee osteoarthritis. However, visual assessment does not capture thrust severity and quantification with optical motion capture is often not feasible. Inertial sensors may provide a convenient alternative to optical motion capture. This proof-of-concept study sought to compare wearable inertial sensors to optical motion capture for the quantification of varus thrust., Methods: Twenty-six participants with medial knee osteoarthritis underwent gait analysis at self-selected and fast speeds. Linear regression with generalized estimating equations assessed associations between peak knee adduction velocity or knee adduction excursion from optical motion capture and peak thigh or shank adduction velocity from two inertial sensors on the lower limb. Relationships between inertial measures and peak external knee adduction moment were assessed as a secondary aim., Findings: Both thigh and shank inertial sensor measures were associated with the optical motion capture measures for both speeds (P < 0.001 to P = 0.020), with the thigh measures having less variability than the shank. After accounting for age, sex, body mass index, radiographic severity, and limb alignment, thigh adduction velocity was also associated with knee adduction moment at both speeds (both P < 0.001)., Interpretation: An inertial sensor placed on the mid-thigh can quantify varus thrust in people with medial knee osteoarthritis without the need for optical motion capture. This single sensor may be useful for risk screening or evaluating the effects of interventions in large samples., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Varus thrust during walking is common in people with knee osteoarthritis (OA) and can increase the risk of disease progression. Lower limb muscle strength, particularly isokinetic strength, may play a role in varus thrust. The purpose of this study was to investigate the relationships between varus thrust and isokinetic strength knee extensors and flexors in people with and without knee OA . Data from participants with (n = 28) and without (n = 68) knee OA were used. All participants underwent gait analysis during walking at a self-selected pace. Isokinetic knee extensor and flexor strength were measured at 60°/s and 120°/s. Varus thrust was measured using peak knee adduction velocity and knee adduction excursion during the first half of stance. Multiple linear regression was used to examine relationships between strength and varus thrust in the two groups separately while adjusting for age, sex, walking speed, and static alignment. In those with knee OA, knee extensor (Model R 2  = 27.0%) and flexor torque (Model R 2  = 28.5%) at 60°/s were negatively associated with peak adduction velocity. No other associations were seen between strength and varus thrust measures in the OA group. No associations were seen in the control group. Lower isokinetic knee extensor and flexor muscle at 60°/s strength is related to greater magnitude of varus thrust in individuals with knee OA but not in those without OA. Isokinetic strength deficits may be involved in varus thrust., Competing Interests: Declaration of Competing Interest None of the authors report any conflicts with this research., (Copyright © 2020 Elsevier Ltd. All rights reserved.)