Implications of using hierarchical and six degree-of-freedom models for normal gait analyses

Buczek, Frank L.; Rainbow, Michael J.; Cooney, Kevin M.; Walker, Matthew R.; Sanders, James O.

doi:10.1016/j.gaitpost.2009.08.245

Cited by 45 publications

(32 citation statements)

References 24 publications

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“…Figure 5 shows the hip angle in the frontal axis representing the flexion/extension motion. The results show a same pattern and range of values found in the literature for the human gait motion (Ferrari et al 2008;Buczek et al 2010). …”

Section: Resultssupporting

confidence: 83%

A computational model for dynamic analysis of the human gait

Vimieiro

Andrada

Witte

et al. 2013

Computer Methods in Biomechanics and Biomedical Engineering

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Biomechanical models are important tools in the study of human motion. This work proposes a computational model to analyse the dynamics of lower limb motion using a kinematic chain to represent the body segments and rotational joints linked by viscoelastic elements. The model uses anthropometric parameters, ground reaction forces and joint Cardan angles from subjects to analyse lower limb motion during the gait. The model allows evaluating these data in each body plane. Six healthy subjects walked on a treadmill to record the kinematic and kinetic data. In addition, anthropometric parameters were recorded to construct the model. The viscoelastic parameter values were fitted for the model joints (hip, knee and ankle). The proposed model demonstrated that manipulating the viscoelastic parameters between the body segments could fit the amplitudes and frequencies of motion. The data collected in this work have viscoelastic parameter values that follow a normal distribution, indicating that these values are directly related to the gait pattern. To validate the model, we used the values of the joint angles to perform a comparison between the model results and previously published data. The model results show a same pattern and range of values found in the literature for the human gait motion.

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Section: Resultssupporting

confidence: 83%

A computational model for dynamic analysis of the human gait

Vimieiro

Andrada

Witte

et al. 2013

Computer Methods in Biomechanics and Biomedical Engineering

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“…During walking and other motor tasks, measurement inaccuracy associated with skin markers can exceed considerably the true skeletal motion [21,29,30,[33][34][35]. In particular, because of the distribution of the soft tissues about the long bones of the lower limb, joint axial rotation is much affected [21,29].…”

Section: Discussionmentioning

confidence: 99%

Range of motion and repeatability of knee kinematics for 11 clinically relevant motor tasks

Desloovere

Wong²,

Swings

et al. 2010

Gait & Posture

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Standard gait analysis reports knee joint rotations in the three anatomical planes without addressing their different levels of reliability. Most clinical studies also restrict analysis to knee flexion-extension, because knee abduction-adduction and axial rotation are small with respect to the corresponding amount of measurement artefact. This study analyses a set of 11 motor tasks, in order to identify those that are adequately repeatable and that can induce greater motion at the knee than walking. Ten volunteers (mean ± SD age: 29 ± 9 years) each underwent three motion analysis sessions on different days with a standard gait analysis system and protocol. In each session they performed normal walking, walking with sidestep and crossover turns, ascent onto and descent off a step, descent with sidestep and crossover turns, chair rise, mild and deep squats, and lunge. Range and repeatability of motions in the three anatomical planes were compared by ANOVA. The sidestep turns showed a range of axial rotation significantly larger than that in walking (about 8°), while maintaining similar levels of repeatability. Ascent, chair rise, squat, and lunge showed greater flexion ranges than walking; among these, ascent was the most repeatable. The results show that turning increases knee axial rotation in young subjects significantly. Further, squats and lunges, currently of large interest in orthopaedics and sports research, have smaller repeatability, likely accounted for to the smaller constraints than in the traditional motor tasks.

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“…These marker clusters were placed on the thigh and shank to track the six degree-of-freedom motions of the femur and tibia using OMC. This marker set was chosen because it is a subset of a larger marker set commonly used to track segment motion using OMC (Buczek et al, 2010). The outfitted leg was chosen randomly (6L and 4R).…”

Section: Methodsmentioning

confidence: 99%

Kinematic differences between optical motion capture and biplanar videoradiography during a jump–cut maneuver

Miranda

Rainbow

Crisco

et al. 2013

Journal of Biomechanics

Self Cite

118

115

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Jumping and cutting activities are investigated in many laboratories attempting to better understand the biomechanics associated with non-contact ACL injury. Optical motion capture is widely used; however, it is subject to soft tissue artifact (STA). Biplanar videoradiography offers a unique approach to collecting skeletal motion without STA. The goal of this study was to compare how STA affects the six-degree-of-freedom motion of the femur and tibia during a jump-cut maneuver associated with non-contact ACL injury. Ten volunteers performed a jump-cut maneuver while their landing leg was imaged using optical motion capture (OMC) and biplanar videoradiography. The within-bone motion differences were compared using anatomical coordinate systems for the femur and tibia, respectively. The knee joint kinematic measurements were compared during two periods: before and after ground contact. Over the entire activity, the within-bone motion differences between the two motion capture techniques were significantly lower for the tibia than the femur for two of the rotational axes (flexion/extension, internal/external) and the origin. The OMC and biplanar videoradiography knee joint kinematics were in best agreement before landing. Kinematic deviations between the two techniques increased significantly after contact. This study provides information on the kinematic discrepancies between OMC and biplanar videoradiography that can be used to optimize methods employing both technologies for studying dynamic in vivo knee kinematics and kinetics during a jump-cut maneuver.

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Implications of using hierarchical and six degree-of-freedom models for normal gait analyses

Cited by 45 publications

References 24 publications

A computational model for dynamic analysis of the human gait

A computational model for dynamic analysis of the human gait

Range of motion and repeatability of knee kinematics for 11 clinically relevant motor tasks

Kinematic differences between optical motion capture and biplanar videoradiography during a jump–cut maneuver

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