Gait biometrics in children with cerebral palsy before and after robotic mechanotherapy

Abstract

Background. The improvement in existing methods and the development of new principles for treating children with cerebral palsy necessitates a quantitative assessment of the parameters of motor activity. However, because of the explicit and complex abnormalities in motor skills in patients with severe forms of cerebral palsy, an evaluation of their locomotor function dynamics using instrumental diagnostic methods remains a serious problem.
 Aim. This work aimed to study the walking function in patients with cerebral palsy before and after motor rehabilitation using a biomechanical method with biometric sensors.
 Materials and methods. We examined 14 patients with cerebral palsy aged 8 to 13 years with III level of restriction of motor activity according to the gross motor function classification system (GMFCS). All patients underwent rehabilitation in the Lokomat robotic simulator for three weeks. The course consisted of 15 sessions of 45 min each. The temporal and dynamic parameters of walking were studied in 14 patients with cerebral palsy before and after a course of locomotor training. The biometry of the step cycle was studied using the STEDIS hardware-software complex, including the Neurosens set of wireless biometric sensors. The temporal characteristics of the step cycle and the force interaction of the lower extremities with the supporting surface during walking were recorded. For comparison, we conducted a biomechanical examination of 18 healthy children of the same age who did not have signs of orthopedic disorders.
 Results. Although after a course of mechanotherapy, the indices of the support phases in biometry in children with cerebral palsy did not reach the level of healthy individuals, a physiological tendency to roll foot was observed in the phase of pushing and accelerating the foot. Active braking of the lower limb increased. The studied time parameters showed a relative improvement in the step structure because of the emerging tendency to normalize the ratio of the periods of the double support of the contralateral lower extremities.
 Conclusion. Robotic mechanotherapy helps to change the biomechanical pattern of walking of a child with a severe degree of cerebral palsy. An instrumental analysis of walking using wireless biometric sensors allows you to evaluate the results and effectiveness of rehabilitation measures in patients with severe motor impairment objectively.