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：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（MLP，多层感知机）预测三维地面反作用力信号。

方法：招募17名健康受试者，令其以自主选择的步速行走，于小腿远端前侧佩戴三维加速度计；在步道中段嵌入测力台。选取含1个隐藏层与3个输出层的MLP神经网络，用于模拟前后向（AP）、垂直向（Vert）及内外向（ML）地面反作用力（GRF）。输入层包含14个由加速度计信号提取的输入特征，均基于既往研究筛选得出。采用主成分分析（PCA）对比模拟信号与实测信号的波形曲线，并计算两类信号间的皮尔逊相关系数与平均绝对偏差（MAD）。

结果：PCA分析结果显示，在前后向与内外向GRF的加载响应阶段，模拟信号与实测信号间存在微小但具有统计学意义的差异；垂直向GRF则未出现此类差异。模拟信号与实测信号间的相关性较高（前后向：0.97；垂直向：0.98；内外向：0.80）。前后向GRF的MAD为受试者体重的1.8%，垂直向为4.5%，内外向为1.4%。

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间的高度非线性关联，与既往采用足底压力装置开展的相关研究结果一致。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。