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Research on Biomedical Engineering

versão impressa ISSN 2446-4732versão On-line ISSN 2446-4740

Resumo

LEPORACE, Gustavo; BATISTA, Luiz Alberto  e  NADAL, Jurandir. Prediction of 3D ground reaction forces during gait based on accelerometer data. Res. Biomed. Eng. [online]. 2018, vol.34, n.3, pp.211-216.  Epub 21-Set-2018. ISSN 2446-4740.  https://doi.org/10.1590/2446-4740.06817.

Introduction

The aim of this study was to predict 3D ground reaction force signals based on accelerometer data during gait, using a feed-forward neural network (MLP).

Methods

Seventeen healthy subjects were instructed to walk at a self-selected speed with a 3D accelerometer attached to the distal and anterior part of the shank. A force plate was embedded into the middle of the walkway. MLP neural networks with one hidden layer and three output layers were selected to simulate the anteroposterior (AP), vertical (Vert) and mediolateral (ML) ground reaction forces (GRF). The input layer was composed of fourteen inputs obtained from accelerometer signals, selected based on previous studies. Principal component analysis (PCA) was used to compare the simulated and collected curves. The Pearson correlation coefficient and the mean absolute deviation (MAD) between signals were calculated.

Results

PCA identified small, but significant differences between collected and simulated signals in the loading response phases of AP and ML GRF, while Vert did not show differences. The correlation between the simulated and collected signals was high (AP: 0.97; Vert: 0.98; ML: 0.80). MAD was 1.8%BW for AP, 4.5%BW for Vert and 1.4%BW for ML.

Conclusion

This study confirmed that multilayer perceptron neural network can predict the highly non-linear relationship of shank acceleration parameters and ground reaction forces, as well as other studies have done using plantar pressure devices. The greater advantages of this device are the low cost and the possibility of use outside the laboratory environment.

Palavras-chave : Acceleration; Force; Gait; Feedforward neural network; Simulation.

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