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Finger muscle attachments for an OpenSim upper-extremity model.

Lee JH, Asakawa DS, Dennerlein JT, Jindrich DL - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona, United States of America.

ABSTRACT
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.

No MeSH data available.


Ring finger moment arm values (mm).Left MCP curves, PIP and DIP curves represent flex/extension moment arms as a function of flexion (+)/ extension (-). Right MCP curves represent ab/adduction moment arms as a function of abduction (+)/ adduction (-). Solid curves (with plot markers) represent experimentally derived moment arms from anatomical attachment locations, and dotted curves represent optimally estimated moment arms from data-driven optimizations.
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pone.0121712.g008: Ring finger moment arm values (mm).Left MCP curves, PIP and DIP curves represent flex/extension moment arms as a function of flexion (+)/ extension (-). Right MCP curves represent ab/adduction moment arms as a function of abduction (+)/ adduction (-). Solid curves (with plot markers) represent experimentally derived moment arms from anatomical attachment locations, and dotted curves represent optimally estimated moment arms from data-driven optimizations.


Finger muscle attachments for an OpenSim upper-extremity model.

Lee JH, Asakawa DS, Dennerlein JT, Jindrich DL - PLoS ONE (2015)

Ring finger moment arm values (mm).Left MCP curves, PIP and DIP curves represent flex/extension moment arms as a function of flexion (+)/ extension (-). Right MCP curves represent ab/adduction moment arms as a function of abduction (+)/ adduction (-). Solid curves (with plot markers) represent experimentally derived moment arms from anatomical attachment locations, and dotted curves represent optimally estimated moment arms from data-driven optimizations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4390324&req=5

pone.0121712.g008: Ring finger moment arm values (mm).Left MCP curves, PIP and DIP curves represent flex/extension moment arms as a function of flexion (+)/ extension (-). Right MCP curves represent ab/adduction moment arms as a function of abduction (+)/ adduction (-). Solid curves (with plot markers) represent experimentally derived moment arms from anatomical attachment locations, and dotted curves represent optimally estimated moment arms from data-driven optimizations.
Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, Arizona State University, Tempe, Arizona, United States of America.

ABSTRACT
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.

No MeSH data available.