SimCP: A Simulation Platform to Predict Gait Performance Following Orthopedic Intervention in Children With Cerebral Palsy

Abstract

Gait deficits in cerebral palsy (CP) are often treated with a single-event multi-level surgery (SEMLS). Selecting the treatment options (combination of bony and soft tissue corrections) for a specific patient is a complex endeavor and very often treatment outcome is not satisfying. A deterioration in 22.8% of the parameters describing gait performance has been reported and there is need for additional surgery in 11% of the patients. Computational simulations based on musculoskeletal models that allow clinicians to test the effects of different treatment options before surgery have the potential to drastically improve treatment outcome. However, to date, no such simulation and modeling method is available. Two important challenges are the development of methods to include patient-specific neuromechanical impairments into the models and to simulate the effect of different surgical procedures on post-operative gait performance. Therefore, we developed the SimCP framework that allows the evaluation of the effect of different simulated surgeries on gait performance of a specific patient and includes a graphical user interface (GUI) that enables performing virtual surgery on the models. We demonstrated the potential of our framework for two case studies. Models reflecting the patient-specific musculoskeletal geometry and muscle properties are generated based solely on data collected before the treatment. The patient's motor control is described based on muscle synergies derived from pre-operative EMG. The GUI is then used to modify the musculoskeletal properties according to the surgical plan. Since SEMLS does not affect motor control, the same motor control model is used to define gait performance pre- and post-operative. We use the capability gap (CG), i.e., the difference between the joint moments needed to perform healthy walking and the joint moments the personalized model can generate, to quantify gait performance. In both cases, the CG was smaller post- then pre-operative and this was in accordance with the measured change in gait kinematics after treatment.

Predicting walking response to ankle exoskeletons using data-driven models

“…Additional dimensionality reduction, such as via sparse regression, may reduce the LPV model's complexity and demand for training data [25,31,46]. However, when only one training condition or a few strides are collected, as is standard in clinical gait analysis, phasevarying model predictions will be poor and physiologically-detailed or population-specific models may generate more accurate predictions [8,19,44,45].…”

“…While individuals explore different gait patterns to identify an energetically-optimal gait, exploration does not always occur spontaneously, resulting in suboptimal gait patterns for some users [17]. Popular physiologically-detailed models of human gait typically assume instantaneous and optimal adaptation, which do not reflect how experience and exploration may influence responses to exoskeletons, possibly reducing the accuracy of predicted responses [18,19].…”

Physics-Based Simulations to Predict the Differential Effects of Motor Control and Musculoskeletal Deficits on Gait Dysfunction in Cerebral Palsy: A Retrospective Case Study

“…Fourth, we assumed that resistance encountered when evaluating the ROM during the clinical examination may be, at least in part, attributed to passive muscle forces. Hence, we included a term in the cost function minimizing the difference between fiber lengths at these extreme positions of the ROM and reference fiber lengths generating large passive forces (Pitto et al, 2019). Finally, we minimized optimal fiber lengths, assuming that children with CP have short fibers (Barrett and Lichtwark, 2010).…”

Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies

“…Prediction of muscle forces can provide valuable insight not only for understanding the control strategies employed by the central nervous system (CNS) (Contessa and Luca, 2013;Del Vecchio et al, 2018), but also for developing effective treatments for neuromusculoskeletal disorders (Shao et al, 2009;Fregly et al, 2012bFregly et al, , 2012aAllen et al, 2013;Pitto et al, 2019;Sauder et al, 2019) Since direct measurement of muscle force is invasive using current technology, computational techniques has been developed to generate muscle force estimates (Anderson and Pandy, 2001;Lloyd and Besier, 2003;Thelen et al, 2003;Buchanan et al, 2005;Shao et al, 2009) However, since the human musculoskeletal system possesses more muscles than degrees-of-freedom (DOFs) in the skeleton (i.e., the muscle redundancy problem), no unique solution for muscle exists unless muscle activity patterns are defined by measured EMG signals (Lloyd and Besier, 2003;Manal and Buchanan, 2003;Buchanan et al, 2005;Shao et al, 2009;Kumar et al, 2012;Sartori et al, 2012;Meyer et al, 2017), or assumptions are made about how muscles contribute to the joint moments, such as minimization of energetic cost (Anderson and Pandy, 2001; Ackermann and van den Bogert, 2010; S. Shourijeh and McPhee, 2014). EMG-driven musculoskeletal modeling is a computational approach for predicting muscle forces that can bypass the muscle redundancy problem while simultaneously allowing for calibration of unmeasurable musculotendon properties (e.g., optimal muscle fiber length) (Lloyd and Besier, 2003;Amarantini and Martin, 2004;Shao et al, 2009;Sartori et al, 2012;Meyer et al, 2017).…”