RESUMO
Resumen: La Parálisis Cerebral (PC) es un grupo de trastornos pre, post y perinatales permanentes del desarrollo, movimiento y postura debidos a alteraciones no progresivas ocurridas durante el desarrollo cerebral, producto de lesiones del Sistema Nervioso Central. Debido a la importancia del uso del miembro superior en las actividades de la vida diaria, es importante considerar formas eficientes de medir el desempeño motor de este miembro en los pacientes con PC. Una forma de obtener la evaluación del miembro torácico es grabando movimientos definidos y calculando la suavidad de los mismos, utilizando un tablero seleccionador de figuras instrumentado. Nuestro objetivo es desarrollar un protocolo de valoración para el miembro superior, que a su vez sea objetivo, eficiente y que otorgue una medición cuantitativa del grado de afectación motora de los niños con PC en un entorno clínico.
Abstract: Cerebral Palsy (CP) is a group of permanent pre, post and perinatal disorders of the motor and posture development due to non-progressive alterations in brain's natural development caused by injuries in the Central Nervous System. Due to the importance of the daily use of the upper limb members, it's important to consider more efficient ways to evaluate the performance in patients diagnosed with CP. One way to obtain an evaluation of the performance of the thoracic member is recording defined movements and calculating the smoothness, using an instrumented sorting block box. Our objective is to create a protocol of valuation for the upper member that is objective, efficient and that gives a quantitative feedback of the grade of the motor affectation of child with PC in a clinical environment.
RESUMO
The purpose of this study is to develop a system capable of performing calculation of temporal gait parameters using two low-cost wireless accelerometers and artificial intelligence-based techniques as part of a larger research project for conducting human gait analysis. Ten healthy subjects of different ages participated in this study and performed controlled walking tests. Two wireless accelerometers were placed on their ankles. Raw acceleration signals were processed in order to obtain gait patterns from characteristic peaks related to steps. A Bayesian model was implemented to classify the characteristic peaks into steps or nonsteps. The acceleration signals were segmented based on gait events, such as heel strike and toe-off, of actual steps. Temporal gait parameters, such as cadence, ambulation time, step time, gait cycle time, stance and swing phase time, simple and double support time, were estimated from segmented acceleration signals. Gait data-sets were divided into two groups of ages to test Bayesian models in order to classify the characteristic peaks. The mean error obtained from calculating the temporal gait parameters was 4.6%. Bayesian models are useful techniques that can be applied to classification of gait data of subjects at different ages with promising results.