An improved neural network algorithm for acceleration sensor to recognize human posture

Lv, Liping

doi:10.1080/09720529.2018.1449318

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Este operador aprende un modelo por medio de una red neuronal feed-forward entrenada por un algoritmo de propagación hacia atrás (perceptrón multicapa). Se ha utilizado este algoritmo para diferentes investigaciones como el reconocimiento de posturas humanas [21]. Para la prueba se utilizó los siguientes parámetros: tamaño de capas ocultas: 2, ciclos de entrenamiento: 200, tasa de aprendizaje: 0.01, y momentum: 0.9.…”

Section: F Improved Neural Netunclassified

Clasificación de niveles de Medida de la Función Motora Gruesa mediante técnicas de Aprendizaje Automático

Pineda¹,

Torres²,

Sulla-Torres³

2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

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The aim of the article is to classify the levels of gross motor function measurement (GMFCS) in minors using machine learning techniques. The study elements were 16 patients, boys, and girls between 2 and 9 years of age from a rehabilitation and physiotherapy institution suffering from cerebral palsy in gross motor function. The clinical analysis, the application of therapy and its measurement of gross motor function were collected, then the classification of nine machine learning algorithms was applied: k-Nearest Neighbor (k-NN), Gradient Boosted tree, Decision Stump, Random Tree, Rule Induction, Improved Neural Net, Generalized Linear Model, SVM, and Linear Discriminant Analysis, which were compared based on accuracy. The results obtained showed that the Linear Discriminant Model was the one that gave the best result with a 96.88 classification accuracy. Therefore, it is concluded that the use of machine learning techniques allows obtaining good accuracy in the classification of the measured level of gross motor function in boys and girls that can be used by specialists to carry out this task.

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Section: F Improved Neural Netunclassified