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Background Total knee arthroplasty (TKA) is the treatment of choice for end-stage knee osteoarthritis. Postoperative static knee alignment has been recognized as a key component of successful surgery. A correction toward the kinematics of a native knee is expected after TKA, with an aim for neutral mechanical alignment. The evolution of frontal plane knee kinematics is not well understood. Methods Nineteen patients awaiting TKA were recruited. Three-dimensional knee kinematics during treadmill gait were assessed pre-operatively, 12 months after surgery, and compared to a control group of 17 asymptomatic participants. Results Mean radiographic mechanical alignment was corrected from 5.4° ± 5.0 (Standard Deviation) varus pre-operatively to 0.1° ± 2.0 (Standard Deviation) valgus postoperatively (P = 0.002). Mean stance coronal plane alignment decreased from 6.7° ± 4.0 (Standard Deviation) varus per-operatively to 2.1° ± 3.8 (Standard Deviation) postoperatively (P = 0.001). Correlation between radiographic mechanical axis angle and dynamic frontal plane alignment during gait, before and after surgery, was weak (pre-operative R = 0.41; postoperative R = 0.13) compared to control (R = 0.88). In the sagittal plane, TKA patients maintained their pre-operative stiff knee gait adaptation. Postoperative transverse plane kinematics suggested restoration of external tibial rotation during swing after TKA compared to control (Pre-operative 3.1°, postoperative 6.8°, control 7.1°, P = 0.05). Conclusion The lack of correlation between static and dynamic alignment suggests that static radiographic coronal alignment of the knee does not accurately predict dynamic behavior. In the sagittal plane, pre-operative gait adaptations were still present 12 months after surgery, supporting the need for a functional assessment to guide postoperative rehabilitation following TKA.

A large amount of data including joint kinematics, joint kinetics, clinical and functional measurements constitutes the clinical gait analysis basis which is a process whereby quantitative gait information are collected to aid in clinical decision-making. Therefore, better understanding the relationship between the biomechanical and clinical data for the knee osteoarthritis (OA) patient is for a relevant importance. It’s the purpose of this paper, which aims to analyze and visualize the correlation structure between biomechanical characteristics and clinical symptoms, and thus to provide an additional knowledge from the coupling of these parameters that will be useful for the pathology assessment of knee-joint disease in the end-staged knee OA patients. We perform two multivariate statistical approaches, first, a Canonical Correlation Analysis (CCA) to assess the multivariate association and, second, a graphical- based representation of the multivariate correlation to better understand the association between these multivariate data. Results show the usefulness of using such multivariate approaches to highlight association and specific correlation structure between the features and to extract meaningful information.

This paper aims to analyze the correlation structure between the kinematic and clinical parameters of an end-staged knee osteoarthritis population. The kinematic data are a set of characteristics derived from 3D knee kinematic patterns. The clinical parameters include the answers of a clinical questionnaire and the patient&rsquo, s demographic characteristics. The proposed method performs, first, a regularized canonical correlation analysis (RCCA) to evaluate the multivariate relationship between the clinical and kinematic datasets, and second, a combined visualization method to better understand the relationships between these multivariate data. Results show the efficiency of using different and complementary visual representation tools to highlight hidden relationships and find insights in data.

Objective To investigate, as a discovery phase, if 3D knee kinematics assessment parameters can serve as mechanical biomarkers, more specifically as diagnostic biomarker and burden of disease biomarkers , as defined in the Burden of Disease, Investigative, Prognostic, Efficacy of Intervention and Diagnostic classification scheme for osteoarthritis (OA) ( Altman et al., 1986 ). These biomarkers consist of a set of biomechanical parameters discerned from 3D knee kinematic patterns, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, during gait recording. Methods 100 medial compartment knee OA patients and 40 asymptomatic control subjects participated in this study. OA patients were categorized according to disease severity, by the Kellgren and Lawrence grading system. The proposed biomarkers were identified by incremental parameter selection in a regression tree of cross-sectional data. Biomarker effectiveness was evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity and specificity. Results Diagnostic biomarkers were defined by a set of 3 abduction/adduction kinematics parameters. The performance of these biomarkers reached 85% for the AUC, 80% for sensitivity and 90% for specificity; the likelihood ratio was 8%. Burden of disease biomarkers were defined by a 3-decision tree, with sets of kinematics parameters selected from all 3 movement planes. Conclusion The results demonstrate, as part of a discovery phase, that sets of 3D knee kinematic parameters have the potential to serve as diagnostic and burden of disease biomarkers of medial compartment knee OA.

One of the main motivations for classifying knee kinematic signals, namely the variation during a locomotion gait cycle of the angles the knee makes with respect to the three-dimensional (3D) planes of flexion/extension, abduction/adduction, and internal/external rotation, is to assist diagnosis of knee pathologies. These signals are informative but high dimensional, and highly variable, which has posed difficulties that have been addressed by machine learning algorithms. The purpose of this study is to investigate classification of knee kinematic signals through the entire gait using deep neural networks. The signals are first pre-processed to identify representative patterns, which are then used for deep learning of discriminative classifiers. This paper describes an efficient means of distinguishing between knee osteoarthrisis patients and asymptomatic participants, and our methods and experiments which validate it.

Background Inertial motion capture (IMC) is rapidly gaining in popularity to evaluate gait in clinical settings. Previous examinations of IMC knee kinematics were often limited to the sagittal plane and IMC calibration has not been thoroughly investigated. Research question The objective was to validate IMC 3D knee kinematics calibrated with a double-pose during gait with reference to optical motion capture (OMC). The hypotheses are that IMC can estimate adequately knee kinematics and that both systems will detect similarly the changes with gait speed. Methods Twenty-four healthy participants walked on the treadmill at gait speed of 0.6, 0.8, 1.0 and 1.2 m/s. Knee kinematics were obtained simultaneously with two magnetic and inertial measurement units and passive markers fixed on the KneeKG system. OMC was calibrated with a functional anatomical approach and the IMC with a double-pose. Results Root mean square differences of the two systems yielded 3-6° for knee flexion, adduction and external rotation. Knee kinematics were more similar during the stance phase than the swing phase. Gait speed showed a significant progressive effect on the three knee angles that was similarly detected by the two systems. Significance IMC 3D knee kinematics can be obtained independently with a simple calibration and only two magnetic and inertial measurement units at an acceptable level of error especially during stance.

Measuring knee biomechanics provides valuable clinical information for defining patient-specific treatment options, including patient-oriented physical exercise programs. It can be done by a knee kinesiography test measuring the three-dimensional rotation angles (3D kinematics) during walking, thus providing objective knowledge about knee function in dynamic and weight-bearing conditions. The purpose of this study was to assess whether 3D kinematics can be efficiently used to predict the impact of a physical exercise program on the condition of knee osteoarthritis (OA) patients. The prediction was based on 3D knee kinematic data, namely flexion/extension, adduction/abduction and external/internal rotation angles collected during a treadmill walking session at baseline. These measurements are quantifiable information suitable to develop automatic and objective methods for personalized computer-aided treatment systems. The dataset included 221 patients who followed a personalized therapeutic physical exercise program for 6 months and were then assigned to one of two classes, Improved condition (I) and not-Improved condition (nI). A 10% improvement in pain was needed at the 6-month follow-up compared to baseline to be in the improved group. The developed model was able to predict I and nI with 84.4% accuracy for men and 75.5% for women using a decision tree classifier trained with 3D knee kinematic data taken at baseline and a 10-fold validation procedure. The models showed that men with an impaired control of their varus thrust and a higher pain level at baseline, and women with a greater amplitude of internal tibia rotation were more likely to report improvements in their pain level after 6 months of exercises. Results support the effectiveness of decision trees and the relevance of 3D kinematic data to objectively predict knee OA patients&rsquo, response to a treatment consisting of a physical exercise program.

Background:Various implants of total knee arthroplasty (TKA) are used in clinical practice and each presents specific design characteristics. No implant managed this day to reproduce perfectly the biomechanics of the natural knee during gait.Objectives:We therefore asked whether (1) differences in tridimensional (3D) kinematic data during gait could be observed in two different designs of TKA on the same patients, (2) if those gait kinematic data are comparable with those of asymptomatic knees and (3) if difference in clinical subjective scores can be observed between the two TKA designs on the same patient.Methods:We performed knee kinematic analysis on 15 patients (30 TKAs) with two different TKA implant designs (Nexgen, Zimmer and Triathlon, Stryker) on each knee and on 25 asymptomatic subjects (35 knees). Clinical evaluation included range of motion, weight bearing radiographs, questionnaire of joint perception, KOOS, WOMAC and SF-12.Results:Comparison between TKAs and asymptomatic knees revealed that asymptomatic knees had significantly less knee flexion at initial contact (p< 0.04) and more flexion for most of the swing phase (pbetween 0.004 and 0.04). Asymptomatic knees also had less varus at loading response, during stance phase and during most of the swing phase (pbetween 0.001 - 0.05). Transverse plane analysis showed a tendency for asymptomatic knees to be more in internal rotation during stance phase (p0.02 - 0.04). Comparing both TKA designs, NexgenTMimplant had significantly more flexion at the end of swing phase (p= 0.04) compared to knees with the TriathlonTMimplant. In frontal plane, from initial contact to maximum mid stance angle and between the mean mid stance angle and initial contact NexgenTMTKA had significantly more adduction (varus,p=0.02 – 0.03). Clinical scores of both TKAs did not have significant difference.Conclusions:TKA with the tested implants did not reproduce natural knee kinematics during gait. In our cohort of patients, TKA implant design translated in limited kinematics differences during gait and on clinical results.

Background Biomechanical and clinical parameters contribute very closely to functional evaluations of the knee joint. To better understand knee osteoarthritis joint function, the association between a set of knee biomechanical data and a set of clinical parameters of an osteoarthritis population (OA) is investigated in this study. Methods The biomechanical data used here are a set of characteristics derived from 3D knee kinematic patterns: flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, all determined during gait recording. The clinical parameters include a KOOS questionnaire and the patient’s demographic characteristics. Canonical correlation analysis (CCA) is used (1) to evaluate the multivariate relationship between biomechanical data and clinical parameter sets, and (2) to cluster the most correlated parameters. Multivariate models were created within the identified clusters to determine the effect of each parameter’s subset on the other. The analyses were performed on a large database containing 166 OA patients. Results The CCA results showed meaningful correlations that gave rise to three different clusters. Multivariate linear models were found explaining the subjective clinical parameters by evaluating the biomechanical data contained within each cluster. Conclusion The results showed that a multivariate analysis of the clinical symptoms and the biomechanical characteristics of knee joint function allowed a better understanding of their relationships.

The purpose of this study is to investigate data clustering to determine representative patterns in three-dimensional (3D) knee kinematic data measurements. Kinematic data are high-dimensional vectors to describe the temporal variations of the three fundamental angles of knee rotation during a walking cycle, namely the abduction/adduction angle, with respect to the frontal plane, the flexion/extension angle, with respect to the sagittal plane, and internal/external angle, with respect to the transverse plane. To offset the curse of dimensionality, inherent to high dimensional data pattern analysis, the method reduces dimensionality by isometric mapping without affecting information content. The data thus simplified is then clustered by the DBSCAN algorithm. The method has been tested on a large database of 165 healthy knee kinematic data measurements. Clusters are validated in terms of the silhouette index, the Dunn index, and connectivity. Results show that a two-cluster characterization of the kinematic knee data in each plane is quite effective. A further clinical investigation shows that the men and women knee patterns are balanced between the two clusters and, for 80% of participants, the right and left knees are in the same cluster.

Keywords: Knee Kinematic, Biomechanical Data, Feature Selection, Complexity Measures, Arthroplasty. Abstract: The purpose of this study is to investigate a method to select a set of knee kinematic data fatures to characterize surgical vs nonsurgical arthroplasty subjects. The kinematic features are generated from 3D knee kinematic data patterns, namely, rotations of flexion-extension, abduction-adduction, and tibial internal-external recorded during a walking task on a dedicated treadmill. The discrimination features are selected using three types of statistical complexity measures: the Fisher discriminant ratio, volume of overlap region, and feature efficiency. The interclass distance measurements which the features thus selected induce demonstrate their effectiveness to characterize surgical and nonsurgical subjects for arthroplasty.

Objectives: Gait analysis is a proven method for assessing knee biomechanical adaptations in anterior cruciate ligament deficient (ACLD) patients and to quantify the impact of the reconstructive surgery (ACLR). In an adult population, ACLR has shown partial kinematic correction, as they remain in internal tibial rotation, putting them at risk of rotational instability and develop osteoartitis. ACLD adolescents likely adopt similar gait changes to reduce knee instability, but may show quicker or more return to the normal compared to adults. The purpose of this study is to compare the tridimensional (3D) knee kinematics before and after ACLR in adolescents. Methods: 25 ACLD patients aged between 13 and 19 years old on a waiting list for ACLR were recruited. Bilateral knee 3D kinematic data was gathered during treadmill walking at comfortable self-selected speed using the KneeKGTM System before and 6-9 months after ACLR. Each participant underwent the same surgical protocol (2-strand hamstring tendon graft, hamstring harvest proximally and kept distally, transphyseal tibial tunnel and transepiphyseal femoral tunnel) by the same experienced surgeon. Three analyses were done: ACLD knee pre vs. post-surgery, contralateral healthy knee pre vs. post-surgery, and ACLR knee vs. healthy contralateral knee. Results: In the sagittal and transverse planes, kinematic patterns remained similar to the pre-op; maintaining the characteristic knee flexion gait pattern during single support phase which is well documented in adult ACLD (hamstring facilitation strategy). The ACLR knee exhibited a more valgus dynamic alignment compared to pre-surgery throughout the gait cycle. The healthy contralateral knee kinematics displayed no significant changes in the frontal and sagittal planes after surgery compared to the pre-op pattern. In the transverse plane, post-op gait analysis showed significantly more external rotation at heel-strike and through weight acceptance. While looking at the gait symmetry post-surgery, ACLR knee revealed significantly more valgus while remaining in greater flexion during single limb support. In the transverse plane, an external rotation tendency is observed in the ACLR knee throughout the gait cycle, except during weight acceptance. Conclusion: This study shows that kinematic adaptations found in ACLD adolescents are likely to endure short term after surgery. For example, the greater knee flexion gait pattern used before surgery is still used after ACLR. Interestingly, adolescents used an external tibial rotation adaptation to limit internal tibial instability before the surgery and this pattern was still present post-surgery. Even if theses protection strategies are efficient, the increased dynamical valgus movement seen could place the graft under pressure while increasing patellofemoral stresses, a common complaint after ACLR. Lastly, post-surgery, the contralateral knee seems to mimic the external tibial rotation gait pattern strategy seen in ACL-knee. This study demonstrates that gait protective strategies are still present short term after the surgery. Biomechanical assessment helps ID these adaptations to optimize post-op rehabilitation and prevent secondary injuries.

Up to 20% of total knee arthroplasty patients remain unsatisfied post-surgery, and a large proportion of them report anterior knee pain. This study aims to verify whether patients who experience anterior knee pain after total knee arthroplasty (TKA) will exhibit kinematic characteristics similar to those associated with patellofemoral syndrome, including in the frontal and transverse planes. Using four different assessment methods [radiological, patient-reported outcome, musculoskeletal assessment with functional performance testing, and a 3D kinematic assessment during gait], the clinical and 3D knee kinematic profiles of three groups were compared: a painful and an asymptomatic TKA group and a healthy control group. All three groups underwent a three-dimensional kinematic knee assessment while walking on a treadmill. Prosthetic component rotation was assessed through a CT scan measurement performed by one experienced radiologist. Flexion/extension, ab/adduction, and tibial internal rotation curves were compared, and significant differences were highlighted through ANCOVA analysis performed on SPSS. A total of 62 knees were evaluated, 24 asymptomatic, 21 painful, and 17 control. A dynamic flexion contracture during gait was observed in the painful group, which was associated with a lack of flexibility of the thigh muscles. Moreover, painful TKA cases exhibited a valgus alignment (− 1.5°) during stance, which increases the Q angle and lateralizes the patella. Finally, CT scan evaluation of painful total knee arthroplasty patients revealed that their combined components rotation was in slight internal rotation (− 1.4°, SD 7.0°). Painful TKA patients presented three well-known characteristics that tend to increase patellofemoral forces and that could be the cause of the unexplained pain: a stiff knee gait, a valgus alignment when walking, and combined TKA components slightly internally rotated.

Rigid attachment systems are one of the methods used to compensate for soft tissue artifact (STA) inherent in joint motion analyses.The goal of this study was to quantify STA of an exoskeleton design to reduce STA at the knee, and to assess the accuracy of 3D knee kinematics recorded with the exoskeleton in non-obese and obese subjects during quasi-static weight-bearing squatting activity using biplane radiography.Nine non-obese and eight obese subjects were recruited. The exoskeleton was calibrated on each subject before they performed a quasistatic squatting activity in the EOSThe results showed that STA observed at the femur was larger in non-obese subjects than in obese subjects in frontal rotation (p = 0.004), axial rotation (p = 0.000), medio-lateral displacement (p = 0.000) and antero-posterior displacement (p = 0.019), while STA observed at the tibia was lower in non-obese subjects than in obese subjects for the three rotations (p 0.05) and medio-lateral displacement (p = 0.015). Differences between the markers and bones knee kinematics increased with knee flexion and were similar in both groups, except for abduction-adduction: 4.9° for non-obese subjects against 2.3° for obese subjects (p = 0.011).This study demonstrated that STA at the femur and its impact on knee abduction-adduction using a specific exoskeleton were greater among non-obese subjects than obese subjects, which is encouraging for future biomechanical studies on pathologies such as osteoarthritis.

Introduction/Background Previous studies shown that mechanical factors can be valuable biomarkers to help as a diagnostic aid, risk indicator of osteoarthritis (OA) progression and to be useful in guiding and monitoring treatments. Since the relationship between radiographic Kellgren-Lawrence (KL) grades and knee biomechanics and is not well understood, the study aims at assessing how knee kinematics differ with increasing KL grade. Material and method This cross-sectional study was carried out in Quebec, Canada primary care clinics. Knee OA patients with KL grade ≥ 2 and with pain ≥ 4/10 were recruited. Sociodemographic characteristics and KOOS were compiled. In-clinic 3D knee kinematics was analyzed using the KneeKG™ system. ANCOVAs were performed on 11 kinematic parameters with age as a covariate due to statistical difference between KL4 and KL2 group. A Bonferroni correction was also applied ( α = 0.05/11 = 0.005). Results Two hundred and ninety-four knee OA patients from 62 clinics participated in the study (62.2% women, mean age 63 ± 9.2) ( Table 1 ). KL grade of the most painful knee was equally distributed (34.4% KL2, 35.7% KL3 and 29.9 KL4). Half of participants had bilateral knee OA (52.0%), therefore, kinematic parameters was evaluated on 437 knees. Worst condition of KL4 group was confirmed by KOOS scores ( Table 2 ). Flexion-extension motion during gait was significantly reduced, translating in a more stiff gait pattern, as KL grade increased. KL4 group also exhibited a strongly accentuated dynamic varus alignment and thrust during gait ( Table 3 ). Conclusion Results show that knee function/kinematics of knee OA patients differ with increasing KL grade severity. This reinforces the value adding a functional assessment in the knee OA episode of care to personalize conservative treatment strategy as the progression of OA evolves.

Background: Anterior cruciate ligament (ACL) injuries in adolescents have increased in the past decade. The goal of ACL reconstructive surgery is to restore knee stability, however apart from subjective manual testing, there is no objective method to assess the impact of the treatment on knee stability. In clinical biomechanics, decreased variability in three dimensional (3D) kinematic angles can be associated with increased stability of the joint in performing a movement. Typically, the study of variability of 3D knee kinematics proceeds by reducing what are intrinsically functional responses to a single discrete measurement (e.g. peak flexion angle). As a result, many potentially informative data is ignored.Aim: Prospective gait variability analysis pre- and 6 months post- ACL reconstructive surgery in adolescence patients using Principle Component Analysis (FPCA). Method: FPCA is a multivariate statistical data analysis technique that focuses on treating an entire dataset as functions. Twenty eight ACL adolescent patients pre- and post-operation walked on a treadmill and the 3D knee kinematics were collected (flexion/extension, abduction/adduction and internal/external rotation angles). Functional Principle Component (FPCs) scores of the angle data were extracted to compare variability in gait. Repeated measures of ANOVA and box plots on FPC scores provided evidence of significant difference with decreased variability post-operation for both within-group and within-patients studies.Conclusion:Variability in the 3D knee kinematics have been significantly reduced six months post- versus pre-operation. The proposed functional objective assessment method suggests that ACL reconstructive surgery increases joint stability during walking in adolescents.

The purpose of this article is two-fold : (1) to select a set of bio-mechanical features to characterize arthroplasty candidates and, (2) design a surgical and non-surgical candidate classifier via decision trees. The biomechanical features are generated from 3D knee kinematic patterns, namely, flexion-extension, abduction-adduction, and tibial internal-external rotation measurements taken during gait recordings. The selection of features is done by incremental selection of biomechanical parametes in a classification tree of cross-sectional data. These features are then used to generate decision rules for classification. The effectiveness of the classifier is evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity, and specificity. The classification accuracy is 85% for AUC, 80% for sensitivity, and 90% for specificity. These results demonstrate the effectiveness of the selected biomechanical features and decision tree classifier to perform automatic and objective classification of surgical and non-surgical candidates for arthroplasty.

The complex biomechanics of the human knee joint are the result of an equilibrium of forces exerted by its surrounding soft tissue structures. When one of these forces is removed, as is the case when a ligament ruptures, a redistribution of forces occurs and the biomechanical properties of the knee are altered. One of the most frequently diagnosed knee ligament ruptures is that of the anterior cruciate ligament (ACL). Such a rupture causes increased laxity of the knee joint as well as a complex rotational and translational instability whereby many patients describe a feeling that their knee is slipping, or “giving way.

Background A variety of biomechanical adaptations of the knee during gait have been reported in ACL-deficient patients to cope with anteroposterior knee instability. However, strategies to prevent rotatory knee instability are less recognized. We hypothesized that ACL-deficient patients would make distinctive gait changes to prevent anterolateral rotatory knee instability. Specifically, we hypothesized that during the terminal stance phase of the gait cycle, ACL-deficient patients would reduce the internal rotation knee joint moment and exhibit a higher knee flexion angle. We call this altered gait a pivot-shift avoidance gait . We also hypothesized that patients would not be able to adapt their knee biomechanics as efficiently at a fast gait speed. Methods Twenty-nine patients with chronic ACL deficiency and 15 healthy volunteers took part in a treadmill gait analysis. The terminal stance phase was analyzed under both comfortable and fast gait speed conditions. Findings At both gait speeds, ACL-deficient patients significantly reduced the internal rotation knee joint moment and showed larger knee flexion angles during the terminal stance phase of the gait cycle than did the control group. However, the difference in the minimum knee flexion angle between groups under the fast gait speed condition was not statistically significant. Interpretation ACL-deficient patients adopted the proposed pivot-shift avoidance gait, possibly to prevent anterolateral rotatory knee instability. The patients were not able to adapt their knee biomechanics as effectively during fast-paced walking. This study reinforces the pertinence of gait analysis in ACL-deficient knees to acquire more information about the function of the knee joint.

Background: Numerous studies use a biomechanical assessment to evaluate joint function in knee pathologies such as osteoarthritis. However, most of them focus only on the knee and the consequences of the pathology on other lower limb joints are poorly documented. The objective of this study is to analyze the impact of knee osteoarthritis on ipsilateral hip and ankle joint during gait.Methods: Three-dimension (3D) angular kinematic patterns of the three joints were analyzed on 32 patients diagnosed with knee osteoarthritis (OA) and a control group of 15 asymptomatic subjects (AS). Kinematic data was captured during treadmill gait trials at a self-selected comfortable speed. Analysis of covariance (ANCOVA) was performed on selected points of interest from 3D kinematic patterns of the hip, knee and ankle joints to compare both groups. The significance level was set at p = .05.Results: Gait 3D kinematic gait patterns of OA patients revealed significant differences with those of AS subjects at the three joints.Conclusions: Results suggest that patients with knee osteoarthritis also present alterations in hip and ankle kinematic during gait that should be considered when tailoring conservative treatments.

Context:Decreased flexibility in muscles and joints of lower extremities is commonly observed in runners. Understanding the effect of decreased flexibility on knee walking kinematics in runners is important because, over time, altered gait patterns can make runners vulnerable to overuse injuries or degenerative pathologies.Objectives:To compare hamstring and iliotibial-band (ITB) flexibility and knee kinematics in runners and nonrunners.Design:A descriptive, comparative laboratory study.Setting:Hamstring and ITB flexibility were measured with the active knee-extension test and the modified Ober test, respectively, in both groups of participants. Three-dimensional (3D) walking kinematic data were then recorded at the knee using a motiontracking system.Participants:18 runners and 16 nonrunners.Main Outcome Measures:Knee-extension angle (hamstring flexibility) and hip-adduction angle (ITB flexibility). Knee kinematic parameters of interest included knee angle at initial contact, peak knee angles, and knee-angle range in all planes of movement.Results:The runners had a significantly less flexible ITB than the nonrunners (hip adduction [−] and adduction [+] angles, 3.1° ± 5.6° vs −6.4° ± 4.5°; P < .001). The runners demonstrated a greater mean tibial external-rotation angle at initial contact (7.3° ± 5.8° vs 2.0° ± 4.0°; P = .01) and a smaller mean peak tibial internal-rotation angle (−1.6° ± 3.0° vs −4.2° ± 3.2°; P = .04) than the nonrunners.Conclusion:This study provides new insight into the relationship between muscle flexibility and 3D knee kinematics in runners. This supports the premise that there is an association between muscle flexibility and transverse-plane knee kinematics in this population.

Introduction Gait analysis has been extensively studied by many different disciplines using various methods. Despite numerous attempts to understand the “normal” gait cycle, our current body of knowledge is insufficient to allow us to model normal locomotion in a clinically relevant manner. The primary objective of our study was to characterize normal knee3D kinematics during treadmill gait using the KneeKG™ apparatus. Materials and methods Ninety-five patients, 52 females and 43 males, were recruited to participate in our study. Each patient completed the KOOS and SF-12 questionnaires, underwent a KneeKG™ examination, and had full-length weightbearing (FLWB) x-rays. We analyzed the results of the questionnaires, generated gait cycle 3D kinematics (sagittal, transverse, and frontal plane), and measured clinically relevant angles on the FLWB x-rays. Furthermore, we compared knee kinematics between men and women and between the right and left knees of the same patient. We also attempted to find a correlation, using Pearson correlation statistics, between the hip-knee-ankle angle (HKA) and the frontal plane kinematic. Results Average age of our cohort was 36.1 years (SD: 13.3). Our cohort had scores superior to 98 in all five subsections of the KOOS questionnaire. Knee gait cycle kinematics in all planes was similar to what has been reported in the literature. When comparing men and women, significant differences were found in the sagittal motion and transverse rotation planes and in the entirety of the gait cycle in the frontal plane. When comparing the right and left knees, significant differences were only found in the frontal plane. No meaningful correlations could be made between the HKA angle and abduction. Discussion – conclusion As three-dimensional analysis of human gait progressively becomes a clinically useful tool in musculoskeletal diseases, an understanding of normal gait as well as a creation of a normal gait data bank is essential. We believe the results of our study contribute to understanding the kinematics of the gait cycle and foster the notion that cohorts should be divided for gender to account for gait differences in the frontal plane.

The purpose of this study was to identify meaningful gait patterns in knee frontal plane kinematics from a large population of asymptomatic individuals. The proposed method used principal component analysis (PCA). It first reduced the data dimensionality, without loss of relevant information, by projecting the original kinematic data onto a subspace of significant principal components (PCs). This was followed by a discriminant model to separate the individuals' gait into homogeneous groups. Four descriptive gait patterns were identified and validated by clustering silhouette width and statistical hypothesis testing. The first pattern was close to neutral during the stance phase and in adduction during the swing phase (Cluster 1). The second pattern was in abduction during the stance phase and tends into adduction during the swing phase (Cluster 2). The third pattern was close to neutral during the stance phase and in abduction during the swing phase (Cluster 3) and the fourth was in abduction during both the stance and the swing phase (Cluster 4).

INTRODUCTION In gait analysis, joint forces and moments are computed from force plate data, kinematics data and body segment parameters (BSP). BSP are estimated from predictive or scaling equations, such as de Leva’s equations [1], which are limited to the three principal moments of inertia and to the center of mass’ position along a line joining two adjacent joints. Dumas proposed a method to compute personalized BSP of the thigh thanks to stereoradiography [2]. The object of this work is to combine stereoradiography and gait analysis to set up a patient specific model of the lower limb in order to enhance the accuracy of computed kinetics and improve the clinical analysis.