Biomedical subjects

Jeffrey A Reinbolt

Publications and source records attributed to Jeffrey A Reinbolt.

4 recordsLinked to original sources

PubMed2005

Determination of patient-specific multi-joint kinematic models through two-level optimization.

Dynamic patient-specific musculoskeletal models have great potential for addressing clinical problems in orthopedics and rehabilitation. However, their predictive capability is limited by how well the underlying kinematic model matches the patient's structure. This study presents a general two-level optimization procedure for tuning any multi-joint kinematic model to a patient's experimental movement data. An outer level optimization modifies the model's parameters (joint position and orientations) while repeated inner level optimizations modify the model's degrees of freedom given the current parameters, with the goal of minimizing errors between model and experimental marker trajectories. The approach is demonstrated by fitting a 27 parameter, three-dimensional, 12 degree-of-freedom lower-extremity kinematic model to synthetic and experimental movement data for isolated joint (hip, knee, and ankle) and gait (full leg) motions. For noiseless synthetic data, the approach successfully recovered the known joint parameters to within an arbitrarily tight tolerance. When noise was added to the synthetic data, root-mean-square (RMS) errors between known and recovered joint parameters were within 10.4 degrees and 10 mm. For experimental data, RMS marker distance errors were reduced by up to 62% compared to methods that estimate joint parameters from anatomical landmarks. Optimized joint parameters found using a loaded full-leg gait motion differed significantly from those found using unloaded individual joint motions. In the future, this approach may facilitate the creation of dynamic patient-specific musculoskeletal models for predictive clinical applications.

Biomechanical Phenomena↗

PubMed2005

Evaluation of a particle swarm algorithm for biomechanical optimization.

Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm's global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms--a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units.

Algorithms↗

PubMed2004

Evaluation of parallel decomposition methods for biomechanical optimizations.

As the complexity of musculoskeletal models continues to increase, so will the computational demands of biomechanical optimizations. For this reason, parallel biomechanical optimizations are becoming more common. Most implementations parallelize the optimizer. In this study, an alternate approach is investigated that parallelizes the analysis function (i.e., a kinematic or dynamic simulation) called repeatedly by the optimizer to calculate the cost function and constraints. To evaluate this approach, a system identification problem involving a kinematic ankle joint model was solved using a gradient-based optimizer and three parallel decomposition methods: gradient calculation decomposition, analysis function decomposition, or both methods combined. For a given number of processors, analysis function decomposition exhibited the best performance despite the highest communication and synchronization overhead, while gradient calculation decomposition demonstrated the worst performance due to the fact that the necessary line searches were not performed in parallel. These findings suggest that the method of parallelization most commonly used for biomechanical optimizations may not be the most efficient, depending on the optimization algorithm used. In many applications, the best computational strategy may be to focus on parallelizing the analysis function.

Algorithms↗

PubMed2002

Clinical investigation: endoscopic coronary artery bypass grafting with robotic assistance.

BACKGROUND: The current study reviews clinical feasibility experiences evaluating safety and efficacy of using robotic assistance to create a left internal mammary artery to left anterior descending artery (LIMA-to-LAD) anastomosis. METHODS: Between August and November 1999, 9 patients (aged 54-73 years) underwent robotically assisted endoscopic coronary artery bypass grafting (E-CABG) after institutional review board approval and informed consent were obtained. The robotics were transthoracically introduced in the fifth and sixth intercostal spaces. The LIMA-to-LAD anastomoses were endoscopically constructed with robotic assistance, and patency was assessed by flow measurement. RESULTS: Each anastomosis was performed robotically, without necessity for intraoperative intervention with traditional techniques. Robotic anastomosis times averaged 29.05 minutes. The robotic system added on average 41.28 minutes to the procedure. LIMA flows prior to anastomoses measured from 11.2 to 29.2 mL/min. LIMA flow measurements following anastomoses averaged 42.07 mL/min. There were no deaths or perioperative myocardial infarctions. CONCLUSIONS: Although E-CABG is an exhaustive and technically demanding procedure, it is feasible for a computerenhanced robotic telemanipulation system to safely and effectively provide substantial assistance to the surgeon completing a thoracoscopic coronary anastomosis.

Aged↗