Our modelling efforts are directed towards two types of modelling.

1. Motion analysis models
2. Computational neuromuscular modelling

Motion analysis models

Motion analysis requires estimating the movement of the skeletal system typically using markers or 6 degree of freedom motion sensors on the persons skin. There are a number of possible errors can affect the accuracy and repeatability of the skeletal motion measurement.

The degree to which these errors affect measurement depends on the computational skeletal model used.

Three different general computational modelling themes exist: anatomical models, functional models and optimal models. We have developed and tested whole body anatomical and functional models, using MRI, motion analysis, computational skeletal models, and animal dissection. The whole body models are a combination of lower body and upper body model for both humans and animals (ostriches, guinea fowl, lizards).

These motion analysis models have been used to study bowling actions in cricket, upperlimb function in children with cerebral palsy, burns victims, and patients who have different types of joint surgeries (knee replacement, autologous chondrocyte implants, partial meniscectomy, ACL reconstructions).

With colleagues at Stanford University we are currently integrating our models into OpenSim to examine how optimal motion analysis modelling and refinement of models using reduced residual analysis improves the results and repeatability of joint motion and moments.

Papers from this research include:

• Campbell A.C., Lloyd D.G., Alderson J.A., Elliott, B.C., MRI development and validation of a new regression model for the estimation of the location of the glenohumeral joint centre of rotation. Journal of Biomechanics, 42(10): 1527-1532, 2009.
• Rubenson J., Lloyd D.G., Besier T.F., Heliams D.B., Fournier P.A., Running in ostriches (Struthio camelus): three-dimensional joint axes alignment and joint kinematics. Journal Of Experimental Biology, 210(14): 2548-2562, 2007.
• Mills P.M., Morrison S., Barrett R.S., Lloyd DG., Repeatability of 3D gait kinematics obtained from an electromagnetic tracking system during treadmill locomotion, Journal of Biomechanics; 40: 1504-1511, 2007.
• Besier T.F., Sturnieks, D.L., Alderson, J.A., Lloyd D.G., Repeatability of gait data using a functional hip joint centre and knee helical axis. Journal of Biomechanics, 36(8): 1159-1168, 2003.
• Lloyd D.G., Alderson J.A, Elliott B.C., An upper limb kinematic model for the examination of cricket bowling: a case study of Muttiah Muralitharan. Journal of Sports Sciences, 18(12): 975-982, 2000.

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Computational neuromuscular skeletal models

The musculoskeletal joint system is mathematically indeterminate, which means that muscles forces cannot be calculated from external loads and motion using inverse dynamics. Extra information must be provided so that external joint loading can be partitioned between muscles, ligaments and the articular surfaces.

Our solution to this problem has been to develop an electromyography driven neuro-musculoskeletal model to estimate muscle forces during static and dynamic tasks. Models have been created for the knee, elbow and ankle joints and have been used in tasks that include walking, running and side stepping.

One version of the model uses a full set of motion analysis data (joint motion, joint moments and electromyography) to scale, calibrate, validate, and predict forces in the muscles and the articular surfaces of the knee.

Another version has been used to predict the motion of the swinging leg to examine how muscles contribute to and control this motion.

With colleagues at Stanford University we are now integrating the electromyography driven neuro-musculoskeletal model into OpenSim. In addition we are speeding up these models so they can work in real-time and to carry out online calibration so that models will calibrate to a person while they are performing tasks. Neuro-musculoskeletal models are also currently being developed for the guinea fowl.

Papers from this research include:

• Winby C.R., Lloyd D.G., Besier T.F. Kirk T.B., Muscle and external load contribution to knee joint contact loads during normal gait. Journal of Biomechanics, 42(14): 22942300, 2009.
• Winby C.R., Lloyd D.G., Kirk T.B., Evaluation of different analytical methods for subject-specific scaling of musculotendon parameters. Journal of Biomechanics, 41(8): 1682-1688, 2008.
• Lloyd D.G., Besier T.F., Winby C.R., Buchanan T.S., Neuromusculoskeletal modelling and simulation of tissue load in the lower extremities. Routledge Handbook of Biomechanics and Human Movement Science, Editors: Y. Hong and R. Bartlett, Taylor & Francis Books Ltd, Oxford, UK., 3-17, 2008.
• Barrett R.S., Besier T.F., Lloyd D.G., Individual muscle contributions to the swing phase of gait: and EMG based forward dynamics model. Simulation Modelling Practice and Theory, 15: 11461155, 2007.
• Lloyd D. G., Buchanan T. S, and Besier, T.F. Neuromuscular Biomechanical Modelling to Understand Knee Ligament Loading. Medicine and Science in Sports and Exercise, Vol 37(11), 19391947, 2005.
• Lloyd D.G. and Besier T.F., An EMG-driven musculoskeletal model for estimation of the human knee joint moments across varied tasks. Journal of Biomechanics, Vol 36(6), 765-776, 2003.
• Lloyd D.G. and Buchanan T.S., Strategies of the muscular support of static varus and valgus loads at the human knee. Journal of Biomechanics, Vol 34(10), 1257-1267, 2001.

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