Resumo

ARENCIBIA CASTELLANOS, Gianna; HERNANDEZ MONTERO, Fidel Ernesto; SERRANO BLANCO, Leisy  e  AZNIELLE RODRIGUEZ, Tania Yadira. Walking speed estimation based on the use of a single inertial measurement unit and an artificial neural network. RCCI [online]. 2022, vol.16, n.2, pp. 1-14.  Epub 01-Jun-2022. ISSN 2227-1899.

This work addresses the estimation of the walking speed by using an artificial neural network (ANN) and a single Inertial Measurement Unit (IMU) placed in the lumbar region, at L3 level. In this context, the main contribution resides in the new features proposed for accomplishing the task, which confer simplicity and effectiveness to the procedure. The ANN application was validated through a database comprising IMU signals recorded from the execution of gait tests on 23 subjects with ages between 20 to 50 years old. In this work, eleven different architectures of multilayer feed-forward ANN were studied in order to choose the most effective one. Cross validation procedure was implemented to assess the quality of the estimation through the computation of the root mean square error (RMSE), the absolute error and the relative error. The most effective model (5-5-1) exhibited a RMSE equal to 0.08 m/s, which is in the range of the results presented in similar studies in this field.

Palavras-chave : Artificial Neural Network; IMU; Walking speed.

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