A sensor-based control strategy for the correction of growth abnormalities

Corrections for growth abnormalities using robotic and computer-aided treatments is a common practice in the medical profession. However, for many therapeutic applications, therapists may use their best judgement in adjusting equipment such as casts or braces. Using the correction of a condition called plagiocephaly in infants as a case study, we provide a corrective, sensor-based control strategy to guide physicians in more accurately correcting for growth abnormalities. In conjunction with an ellipsoidally-shaped mesh-representation of the skull in which the sensors are nodes, we use Model Reference Adaptive Control to regulate the node positions to converge to those of an ideal growth trajectory. We do this both when the error dynamics between the actual and desired systems are known and when they are unknown. We have shown that using a parameter-based adaptation law, stability is achieved in parameter estimation and state convergence; however, when the error dynamics are unknown, stability is only guaranteed for a certain range of control gains. These results show great promise for aiding physicians in more accurately caring for cranial and similar deformalities that occur in infants.