This paper deals with the identification of terrain that is crucial to trigger the damping in semi-active lower limb prosthesis. Objective: To identify level ground and ramp terrains using foot-to-ground angle (FGA) measurement. Methods: First, Instrumented shoe for FGA measurement was developed. Next, data collection from able-bodied (n=5) and amputee (n=1) participants was carried out. Finally, a comparison of identification accuracy using support vector machine (SVM) and convolution neural network (CNN) algorithms was done. Results: The average classification accuracy obtained for able-bodied participants and amputee is 79.57% ± 20.32% and 73.06% ± 12.70%, respectively using SVM, whereas it is 83.45% ± 14.50% and 80% ± 12.15% respectively using CNN. Our off-line analysis shows that overall, CNN outperformed SVM with an average of 4.86% increment in classification accuracy in able-bodied participants and 9.54% in an amputee. This study introduced a simplified, low-cost method for terrain identification in the prosthetic control application.

本文涉及对触发半主动下肢假体阻尼至关重要的地形识别。目标：使用脚对地角（FGA）测量来识别水平地面和坡地地形。方法：首先，开发了用于FGA测量的仪表鞋。接下来，从身体健全（n = 5）和被截肢者（n = 1）的参与者中收集数据。最后，比较了使用支持向量机（SVM）和卷积神经网络（CNN）算法的识别精度。结果：使用SVM，健全参与者和截肢者的平均分类准确度分别为79.57％±20.32％和73.06％±12.70％，而使用CNN分别为83.45％±14.50％和80％±12.15％。我们的离线分析显示，总体而言，CNN的平均表现优于SVM，平均为4。健全参与者的分类准确率提高86％，被截肢者的分类准确率提高9.54％。这项研究介绍了一种简化的低成本方法，用于人工控制应用中的地形识别。