Abstract
This article presents an alternative rehabilitation system based on a visual feedback system for people suffering from cerebral palsy disorder. The proposed feedback system handles textures and movements in a 3D graphic environment, specially designed to develop skills that improve patient performance. The interface is developed in the Unity3D software, identifying patterns of the body is done through motion Kinect and validation of the correct execution of the exercises sensor is carried out, using the technique of machine learning for training rehabilitation system. The experimental results show the efficiency of the system that generates an improvement in the motor abilities of the upper and lower extremities of the patient.
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Acknowledgements
The authors would like to thanks to the Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia–CEDIA for the financing given to research, development, and innovation, through the CEPRA projects, especially the project CEPRA-XI-2017-06; Control Coordinado Multi-operador aplicado a un robot Manipulador Aéreo; also to Universidad de las Fuerzas Armadas ESPE, Universidad Técnica de Ambato, Escuela Superior Politécnica de Chimborazo, Universidad Nacional de Chimborazo, and Grupo de Investigación ARSI, for the support to develop this work.
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Erazo, Y.P., Chasi, C.P., Latta, M.A., Andaluz, V.H. (2019). Machine Learning for Acquired Brain Damage Treatment. In: De Paolis, L., Bourdot, P. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2019. Lecture Notes in Computer Science(), vol 11613. Springer, Cham. https://doi.org/10.1007/978-3-030-25965-5_27
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DOI: https://doi.org/10.1007/978-3-030-25965-5_27
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