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The nervous systems of animals evolved to exert dynamic control of behavior in response to the needs of the animal and changing signals from the environment. To understand the mechanisms of dynamic control, we need a means of predicting how individual neural and body elements will interact to produce the performance of the entire system. We have developed a neuromechanical application named AnimatLab that addresses this problem through simulation. A computational model of a body and nervous system can be constructed from simple components and situated in a virtual world for testing. Simulations and live experiments were used to investigate questions about locust jumping. The neural circuitry and biomechanics of kicking in locusts have been extensively studied. It has been hypothesized that the same neural circuit and biomechanics governed both behaviors, but this hypothesis was not testable with current technology. We built a neuromechanical model to test this and to gain a better understanding of the role of the semi-lunar process (SLP) in jump dynamics. The SLP are bands of cuticle that store energy for use during jumping. The results of the model were compared to a variety of published data and were similar. The SLP significantly increased jump distance, power, total energy, and duration of the jump impulse. Locust can jump precisely to a target, but also exhibit tumbling. We proposed two mechanisms for controlling tumbling during the jump. The first was that locusts adjust the pitch of their body prior to the jump to move the center of mass closer to the thrust vector. The second was that contraction of the abdominal muscles during the jump produced torques that countered the torque due to thrust. There was a strong correlation relating increased pitch and takeoff angle. In simulations there was an optimal pitch-takeoff combination that minimized tumbling that was similar to the live data. The direction and magnitude of tumbling could be controlled by adjusting abdominal tension. Tumbling also influenced jump elevation. Neuromechanical simulation addressed problems that would be difficult to examine using traditional physiological approaches. It is a powerful tool for understanding the neural basis of behavior.

Developmental Dysplasia of the Hip (DDH) is an abnormal condition where hip joint dislocation, misalignment, or instability is present in infants. Rates of incidence of DDH in newborn infants have been reported to vary between 1 and 20 per 1000 births, making it the most common congenital malformation of the musculoskeletal system. DDH early detection and treatment is critical to avoid the use of surgical treatment in infants and to prevent future complications such as osteoarthritis in adult life. To this day several non-surgical treatments involving the use of harnesses and braces have been proposed to treat DDH in infants, with the Pavlik harness being the current non-surgical standard used to treat DDH at early stages. Although the Pavlik harness has been proven to be successful treating subtle dislocations, severe dislocations do not always reduce. Until now the use of the harness remains an empirical method, and its effectiveness often depends on physician expertise or trial-error procedures; thus a clear guideline has not been established to determine the best optimal harness configuration to treat both subtle and severe dislocations. The goal of this dissertation is to understand the connection between reductions for subtle and severe dislocations and passive muscle forces and moments generated while the harness is used during treatment. While the understanding of DDH biomechanics will provide a valuable clinically applicable approach to optimize and increase harness success rate, it is not without its difficulties. This research has created and developed a three-dimensional based on patient-specific geometry of an infant lower limb. The kinematics and dynamics of the lower limb were defined by modeling the hip, femur, tibia, fibula, ankle, foot, and toe bones. The lines of action of five (5) adductor muscles, namely, the Adductor Brevis, Adductor Longus, Adductor Magnus, Pectineus, and Gracilis were identified as mediators of reduction and its mechanical behavior was characterized using a passive response. Four grades (1-4) of dislocation as specified by the International Hip Dysplasia Institute (IHDI) were considered, and the computer model was computationally manipulated to represent physiological dislocations. To account for proper harness modeling, the femur was restrained to move in an envelope consistent with its constraints. The model of the infant lower limb has been used to analyze subtle and severe dislocations. Results are consistent with previous studies based on a simplified anatomically-consistent synthetic model and clinical reports of very low success of the Pavlik harness for severe dislocations. Furthermore the findings on this work suggest that for severe dislocations, the use of the harness could be optimized to achieve hyperflexion of the lower limb leading to successful reduction for cases where the harness fails. This approach provides three main advantages and innovations: 1) the used of patient-specific geometry to elucidate the biomechanics of DDH; 2) the ability to computationally dislocate the model to represent dislocation severity; and 3) the quantification of external forces needed to accomplish reduction for severe dislocations. This study aims to offer a practical solution to effective treatment that draws from engineering expertise and modeling capabilities and also draws upon medical input. The findings of this work will lay the foundation for future optimization of non-surgical methods critical for the treatment of DDH.