Modelling tendon excursions and moment arms of the finger flexors: Anatomic fidelity versus function

Kociolek, Aaron M.; Keir, Peter J.

doi:10.1016/j.jbiomech.2011.05.002

Cited by 19 publications

(13 citation statements)

References 31 publications

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“…For all fingers, we modeled the following muscles: terminal extensor (TE) , extensor slip (ES) , radial band (RB) , ulnar band (UB) , dorsal interosseous or radial interosseous (RI) , lumbricals (LU) , palmar interosseous or ulnar interosseous (UI) , flexor digitorum profundus (FDP) , flexor digitorum superficialis (FDS) and extensor digitorum communis or long extensor (EDC) . The wrapping surfaces for all fingers were based on previous studies [ 9 , 25 , 31 , 35 ]. We modified the wrapping surface parameters (e.g., dimension, orientation and position) to prevent the muscles passing into the bone for the entire range of movements.…”

Section: Methodsmentioning

confidence: 99%

Finger Muscle Attachments for an OpenSim Upper-Extremity Model

et al. 2015

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We determined muscle attachment points for the index, middle, ring and little fingers in an OpenSim upper-extremity model. Attachment points were selected to match both experimentally measured locations and mechanical function (moment arms). Although experimental measurements of finger muscle attachments have been made, models differ from specimens in many respects such as bone segment ratio, joint kinematics and coordinate system. Likewise, moment arms are not available for all intrinsic finger muscles. Therefore, it was necessary to scale and translate muscle attachments from one experimental or model environment to another while preserving mechanical function. We used a two-step process. First, we estimated muscle function by calculating moment arms for all intrinsic and extrinsic muscles using the partial velocity method. Second, optimization using Simulated Annealing and Hooke-Jeeves algorithms found muscle-tendon paths that minimized root mean square (RMS) differences between experimental and modeled moment arms. The partial velocity method resulted in variance accounted for (VAF) between measured and calculated moment arms of 75.5% on average (range from 48.5% to 99.5%) for intrinsic and extrinsic index finger muscles where measured data were available. RMS error between experimental and optimized values was within one standard deviation (S.D) of measured moment arm (mean RMS error = 1.5 mm < measured S.D = 2.5 mm). Validation of both steps of the technique allowed for estimation of muscle attachment points for muscles whose moment arms have not been measured. Differences between modeled and experimentally measured muscle attachments, averaged over all finger joints, were less than 4.9 mm (within 7.1% of the average length of the muscle-tendon paths). The resulting non-proprietary musculoskeletal model of the human fingers could be useful for many applications, including better understanding of complex multi-touch and gestural movements.

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Section: Methodsmentioning

confidence: 99%

Finger Muscle Attachments for an OpenSim Upper-Extremity Model

et al. 2015

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“…A potential limitation in this study is that the scaling factor for the variable moment arms was based on the assumption of the linear proportionality to the volume of the index finger. A recent study suggested a multiple-dimensional scaling that scaled both phalanx lengths and thicknesses resulted in an additive effort to the approximation of the moment arms and excursion of muscles in the index finger (Kociolek and Keir, 2011). Therefore, it is reasonable to assume that the variable moment arms of muscles were proportional to the volume of the index finger in our model.…”

Section: Discussionmentioning

confidence: 99%

“…The moment arms for the seven MTUs vary as ym changes and we used the model derived for the ACT Hand MCP joint to determine the values of moment arms . We assumed that the moment arms are proportional to the volume of index finger (Kociolek and Keir, 2011) and calculated the subject-specific moment arms of the seven MTUs by scaling the ACT hand moment arms with a ratio of the volume of subject's index finger (v) to the ACT hand index finger (V):(rv ¼ v=V, Table 2). The scaled moment arms were also used to calculate the length change of MTUs (lmt ) due to change in ym as: Dlmt i ðymÞ ¼ R i Â ym, where i¼1,y,7 refers to the number of the MTU.…”

Section: Elastic Moment From Mtus (T Mtu )mentioning

confidence: 99%

“…We calculated the subject-specific nominal MTU lengths, l mto , by linearly scaling the l mto values from Holzbaur's model with the volume ratio (r V ) for each subject (Table 2) (Kociolek and Keir, 2011). The tendon slack lengths (l ts ) were functionally adjusted for each subject by implementing the numerical optimization method described in a previous study (Manal and Buchanan, 2004).…”

Section: Elastic Moment From Mtus (T Mtu )mentioning

confidence: 99%

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Muscle-tendon units provide limited contributions to the passive stiffness of the index finger metacarpophalangeal joint

Kuo

Deshpande

2012

Journal of Biomechanics

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“…For example, OpenSim's hand models were used to explore improved routing of tendons for carpal tunnel syndrome and tendon disorders [17]. OpenSim's leg models were utilized to study the pre/postoperative balance associated with quadriceps insertion sites for patients with stiff knee gait [18].…”

Section: B Software Platforms For Biomechanical Modelingmentioning

confidence: 99%

Modeling Implantable Passive Mechanisms for Modifying the Transmission of Forces and Movements Between Muscle and Tendons

Homayouni

Underwood

Beyer

et al. 2015

IEEE Trans. Biomed. Eng.

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Abstract-This paper explores the development of biomechanical models for evaluating a new class of passive mechanical implants for orthopedic surgery. The proposed implants take the form of passive engineered mechanisms, and will be used to improve the functional attachment of muscles to tendons and bone by modifying the transmission of forces and movement inside the body. Specifically, we present how two types of implantable mechanisms may be modeled in the open-source biomechanical software OpenSim. The first implant, which is proposed for hand tendon-transfer surgery, differentially distributes the forces and movement from one muscle across multiple tendons. The second implant, which is proposed for knee-replacement surgery, scales up the forces applied to the knee joint by the quadriceps muscle. This paper's key innovation is that such mechanisms have never been considered before in biomechanical simulation modeling and in surgery. When compared with joint function enabled by the current surgical practice of using sutures to make the attachment, biomechanical simulations show that the surgery with 1) the differential mechanism (tendon network) implant improves the fingers' ability to passively adapt to an object's shape significantly during grasping tasks (2.74× as measured by the extent of finger flexion) for the same muscle force, and 2) the force-scaling implant increases knee-joint torque by 84% for the same muscle force. The critical significance of this study is to provide a methodology for the design and inclusion of the implants into biomechanical models and validating the improvement in joint function they enable when compared with current surgical practice.Index Terms-Biomechanical modeling, implant design, orthopedic surgery.

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Modelling tendon excursions and moment arms of the finger flexors: Anatomic fidelity versus function

Cited by 19 publications

References 31 publications

Finger Muscle Attachments for an OpenSim Upper-Extremity Model

Finger Muscle Attachments for an OpenSim Upper-Extremity Model

Muscle-tendon units provide limited contributions to the passive stiffness of the index finger metacarpophalangeal joint

Modeling Implantable Passive Mechanisms for Modifying the Transmission of Forces and Movements Between Muscle and Tendons

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