Determination of patient-specific multi-joint kinematic models through two-level optimization

Reinbolt, Jeffrey A.; Schutte, Jaco F.; Fregly, Benjamin J.; Koh, Byung Il; Haftka, Raphael T.; George, Alan; Mitchell, Kim H.

doi:10.1016/j.jbiomech.2004.03.031

Cited by 204 publications

(191 citation statements)

References 23 publications

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“…The result is a kinematic representation of the subject entirely based on functional trials. This is similar to the multi-joint method proposed by Reinbolt et al (2005) and minimises the least-squares error between model markers and markers from motion capture experiments. The joint parameters, local positions and rotations, of the hip, knee, ankle and subtalar joint were used as design variables.…”

Section: Kinematically Scaled Modelmentioning

confidence: 93%

“…In terms of the latter, attempts have been made to investigate the sensitivity of joint parameter optimisation to random noise in the marker data (Reinbolt et al 2005). However, Andersen et al (2012) recently demonstrated that marker error caused by STA is neither random nor independent and 95% of the cumulative marker error caused by STA during gait could be modelled with a linear model containing only four parameters.…”

Section: Marker Errorsmentioning

confidence: 99%

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Scaling of musculoskeletal models from static and dynamic trials

Lund

Andersen

Zee

et al. 2015

International Biomechanics

132

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Subject-specific scaling of cadaver-based musculoskeletal models is important for accurate musculoskeletal analysis within multiple areas such as ergonomics, orthopaedics and occupational health. We present two procedures to scale 'generic' musculoskeletal models to match segment lengths and joint parameters to a specific subject and compare the results to a simpler approach based on linear, segment-wise scaling. By incorporating data from functional and standing reference trials, the new scaling approaches reduce the model sensitivity to assumed model marker positions. For validation, we applied all three scaling methods to an inverse dynamics-based musculoskeletal model and compared predicted knee joint contact forces to those measured with an instrumented prosthesis during gait. Additionally, a Monte Carlo study was used to investigate the sensitivity of the knee joint contact force to random adjustments of the assumed model marker positions (+/− one marker diameter). The model based on linear scaling showed the highest variation in the knee joint contact force of 1.44 body weight (BW) around contra-lateral heel strike, and a variation in root mean square deviation (RMSD) of 0.36 BW. The proposed methods reduced the variation to 1.0 BW (RMSD 0.26 BW) for the anatomical landmark based method and 0.47 BW (RMSD 0.06 BW) for the functional based method. Variation in model predictions due to uncertainty in marker positions is a trait of all marker-based musculoskeletal modelling approaches. The presented methods solve part of this problem and rely less on manual identification of anatomical landmarks in the model. The work represents a step towards a more consistent methodology in musculoskeletal modelling.

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Section: Kinematically Scaled Modelmentioning

confidence: 93%

Section: Marker Errorsmentioning

confidence: 99%

Scaling of musculoskeletal models from static and dynamic trials

Lund

Andersen

Zee

et al. 2015

International Biomechanics

132

View full text Add to dashboard Cite

show abstract

“…This method has been applied to computer simulated movements of the lower limbs (Lu and O'Connor, 1999) and the upper limbs (Roux et al, 2002). Other chain models associated with optimisation procedures have been used to analyse gait (Charlton et al, 2004;Reinbolt et al, 2005). In Reinbolt et al (2005) the determination of the kinematics was based on a two-level optimisation and required three markers per segment.…”

Section: Introductionmentioning

confidence: 99%

“…Other chain models associated with optimisation procedures have been used to analyse gait (Charlton et al, 2004;Reinbolt et al, 2005). In Reinbolt et al (2005) the determination of the kinematics was based on a two-level optimisation and required three markers per segment. Performance measures of this algorithm were estimated for 12-DoF synthetic motions.…”

Section: Introductionmentioning

confidence: 99%

Kinematics estimation of straddled movements on high bar from a limited number of skin markers using a chain model

Begon

Wieber

Yeadon

2008

Journal of Biomechanics

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International audienceTo reduce the eﬀects of skin movement artefacts and apparent joint dislocations in the kinematics of whole body movement derived from marker locations, global optimisation procedures with a chain model have been developed. These procedures can also be used to reduce the number of markers when self-occlusions are hard to avoid. This paper assesses the kinematics precision of three marker sets: 16, 11 and 7 markers, for movements on high bar with straddled piked posture. A three-dimensional person-speciﬁc chain model was deﬁned with 9 parameters and 12 degrees of freedom and an iterative procedure optimised the gymnast posture for each frame of the three marker sets. The time histories of joint angles obtained from the reduced marker sets were compared with those from the 16 marker set by means of a root mean square diﬀerence measure. Occlusions of medial markers ﬁxed on the lower limb occurred when the legs were together and the pelvis markers disappeared primarily during the piked posture. Despite these occlusions, reconstruction was possible with 16, 11 and 7 markers. The time histories of joint angles were similar; the main diﬀerences were for the thigh mediolateral rotation and the knee ﬂexion because the knee was close to full extension. When ﬁve markers were removed, the average angles diﬀerence was about 3. This diﬀerence increased to 9 for the seven marker set. It is concluded that kinematics of sports movement can be reconstructed using a chain model and a global optimisation procedure for a reduced number of markers

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“…This method is efficient and can be used for finding multiple solutions for a manipulator. Reinbolt et al (2005) presented a general two-level optimization for fine-tuning joint locations of a kinematic model using mocap data. Since joint locations were registered with respect to the marker, those joints had to be located again once maker positions were changed.…”

Section: Introductionmentioning

confidence: 99%

Two-phase optimized inverse kinematics for motion replication of real human models

Tsai

Lung

2014

Journal of the Chinese Institute of Engineers

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This paper proposes a novel two-phase optimization method to solve the joint variables of a real human model using captured motion data. A dual-mode 3D vision system was used to generate a body geometric model. Twenty-three link segments were separated from the body model. Markers were then affixed on each link for motion capturing by the same vision system. A body kinematic model was then constructed from the geometric model by assigning joint constraints between every two adjacent links. The model contains five kinematic chains with 48 joint freedoms. Joint variables were solved by the first-phase optimization to obtain an appropriate initial posture between two adjacent links. For each kinematic chain, redundant joint variables were solved using the second-phase optimization. By setting proper weightings, the resulting postures of the kinematic model closely match up with those of the motion captured data, while the endpoints can trace the original trajectories as well. Furthermore, the kinematic model offers more reasonable motions than the geometric model that replicates the raw motion data since some excessive movements are corrected by the joint constraints.

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Determination of patient-specific multi-joint kinematic models through two-level optimization

Cited by 204 publications

References 23 publications

Scaling of musculoskeletal models from static and dynamic trials

Scaling of musculoskeletal models from static and dynamic trials

Kinematics estimation of straddled movements on high bar from a limited number of skin markers using a chain model

Two-phase optimized inverse kinematics for motion replication of real human models

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