Prediction of 3D ground reaction forces during gait based on accelerometer data

Abstract 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.

摘要与引言：本研究旨在基于步态过程中的加速度计数据，采用前馈神经网络（多层感知机，Multilayer Perceptron, MLP）预测三维地面反作用力（Ground Reaction Force, GRF）信号。
方法：招募17名健康受试者，要求其以自择步速行走，于小腿远端前侧安装三维加速度计。于步道中段嵌入测力平台。选用含单个隐藏层与三个输出层的多层感知机神经网络，以模拟前后方向（Anteroposterior, AP）、垂直方向（Vertical, Vert）及内外侧方向（Mediolateral, ML）的地面反作用力（GRF）。输入层由14个经既往研究筛选的加速度计信号特征构成。采用主成分分析（Principal Component Analysis, PCA）对比模拟信号与实测信号曲线，并计算信号间的皮尔逊相关系数与平均绝对偏差（Mean Absolute Deviation, MAD）。
结果：主成分分析显示，在前后方向与内外侧方向地面反作用力的加载响应阶段，实测信号与模拟信号间存在微小但具有统计学意义的差异；而垂直方向地面反作用力未表现出此类差异。模拟信号与实测信号间的相关性较高（前后方向：0.97；垂直方向：0.98；内外侧方向：0.80）。平均绝对偏差分别为：前后方向1.8%体重（Body Weight, BW）、垂直方向4.5%体重（BW）、内外侧方向1.4%体重（BW）。
结论：本研究证实，多层感知机神经网络可精准预测小腿加速度参数与地面反作用力之间的高度非线性关系，这与既往采用足底压力设备开展的相关研究结论一致。该装置的显著优势在于成本低廉，且可在实验室环境外使用。