Accurate knowledge of muscle-tendon parameters in biomechanical models is critical for accurate simulation and analyses of human movement. An excellent example of this is the creation of subject-specific models from magnetic resonance imaging (MRI). When Hill-type muscle models are used to calculate muscle forces, the determination of muscle attachment points, optimal fiber length, tendon slack length and maximum isometric force all have a significant influence on the joint moment-angle behavior of the model.In the present study a method was developed for customizing the values of muscle-tendon parameters in a generic model to create subject-specific biomechanical models from MRI. The method was applied by generating musculoskeletal models for the biomechanical simulation platform OpenSim, but the workflow is equally well applicable to other simulation platforms. New computational algorithms are described for identifying joint centers and for reconstructing the centroids of the muscle bellies from MRI. A process is also described for the extraction of the muscle paths and for identifying the positions of 'via-points' used to model muscles wrapping over bones. Finally, a new algorithm is described for adjusting the values of optimal fiber length, tendon slack length and maximum isometric force based on a comparison of the model results with experiment.We tested our computational algorithms by developing subject-specific biomechanical models of five typically developed children (age 9.5 ± 1.7 years) from MRI. The joint moment-angle relationships calculated for the subject-specific models were similar to those determined for corresponding scaled generic models. The results indicate that the proposed methodology is suitable for developing subject-specific models of healthy children. Future studies should investigate how abnormalities of the musculoskeletal system, such as tibial torsion and muscle spasticity, can be integrated into the modeling process.

We developed a method for determining a finite state model of locomotion that is applicable to real-time control of walking in individuals with paralyzed legs. The finite state model represents walking as a set of If-Then rules. An If-Then rule uses coded sensory information as inputs (If) and levels of electrical activities of muscles as outputs (Then). The model incorporates temporal and spatial synergies between muscle groups based on sensory information. The sensory input includes accelerations of leg and body segments, and ground reaction forces at toe and heel zones of the sole. The output of the rules is generated by detecting the onset of muscle activity from the amplified and rectified recordings of EMG signals from the prime movers of the leg. The coding uses a local threshold technique. Adaptive Logic Networks (ALNs) were used for estimation of If-Then rules. The training consisted of various samples of walking recorded in healthy individuals. The application of ALNs was optimized for low misclassification error and fast training. The overall performance of ALN (correct responses that would lead to correct stepping) when applied on test data, not used for the training, was >82%. We assumed that 80% is the margin for correct stepping for the walking in hemiplegic individuals.

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