IEEE Trans Biomed Eng. 2018 Apr;65(4):789-796. doi: 10.1109/TBME.2017.2721300. Epub 2017 Jun 28.
An adaptable lower limb prosthesis with variable stiffness in the transverse plane requires a control method to effect changes in real time during amputee turning. This study aimed to identify classification algorithms that can accurately predict turning using inertial measurement unit (IMU) signals from the shank with adequate time to enact a change in stiffness during the swing phase of gait when the prosthesis is unloaded.
To identify if a turning step is imminent, classification models were developed around activities of daily living including 90° spin turns, 90° step turns, 180° turns, and straight walking using simulated IMU data from the prosthesis shank. Three classifiers were tested: support vector machine (SVM), K nearest neighbors (KNN), and a bagged decision tree ensemble (Ensemble).
Individual training gave superior results over training on a pooled set of users. Coupled with a simple control scheme, the SVM, KNN, and Ensemble classifiers achieved 96%, 93%, and 91% accuracy (no significant difference), respectively, predicting an upcoming turn 400 ± 70 ms prior to the heel strike of the turn. However, classification of straight walking transition steps varied between classifiers at 85%, 82%, 97% (Ensemble significantly different, ), respectively.
The Ensemble model produced the best result overall; however, depending on the priority of identifying turning versus transition steps and processor performance, the SVM or KNN might still be considered.
This research would be useful to help determine a classifier strategy for any lower limb device seeking to predict turn intent.
具有横向可变刚度的适应性下肢假肢需要一种控制方法，以便在截肢者转弯时实时影响刚度的变化。本研究旨在确定分类算法，该算法可以使用假肢小腿的惯性测量单元（IMU）信号准确预测转弯，并且有足够的时间在步态摆动阶段改变刚度，此时假肢处于空载状态。
为了确定是否即将转弯，使用来自假肢小腿的模拟 IMU 数据，围绕日常生活活动（包括 90°旋转转弯、90°步转弯、180°转弯和直走）开发分类模型。测试了三种分类器：支持向量机（SVM）、K 最近邻（KNN）和袋装决策树集成（Ensemble）。
与在用户的集合上进行训练相比，个体训练产生了更好的结果。与简单的控制方案相结合，SVM、KNN 和 Ensemble 分类器在预测即将到来的转弯时，分别以 96%、93%和 91%的准确率（无显著差异），在转弯的脚跟触地前 400±70ms 之前提前预测。然而，分类器在直走过渡步骤的分类上存在差异，准确率分别为 85%、82%和 97%（Ensemble 显著不同）。
总体而言，Ensemble 模型的结果最佳；然而，根据识别转弯与过渡步骤的优先级和处理器性能，SVM 或 KNN 仍可能被考虑。
这项研究将有助于确定任何试图预测转弯意图的下肢设备的分类器策略。
