Woodward Richard B, Spanias John A, Hargrove Levi J
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:6405-6408. doi: 10.1109/EMBC.2016.7592194.
Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming. By using subject independent datasets, whereby a unique subject is tested against a pooled dataset of other subjects, we believe subject training time can be reduced while still achieving an accurate classification. We present here an intent recognition system using an artificial neural network (ANN) with a scaled conjugate gradient learning algorithm to classify gait intention with user-dependent and independent datasets for six unilateral lower limb amputees. We compare these results against a linear discriminant analysis (LDA) classifier. The ANN was found to have significantly lower classification error (P<;0.05) than LDA with all user-dependent step-types, as well as transitional steps for user-independent datasets. Both types of classifiers are capable of making fast decisions; 1.29 and 2.83 ms for the LDA and ANN respectively. These results suggest that ANNs can provide suitable and accurate offline classification in prosthesis gait prediction.
电动下肢假肢能够为截肢患者提供更好的行动能力。此类假肢通常具有预编程模式，可实现诸如爬楼梯和下斜坡等活动，而许多截肢者在使用非电动假肢时很难完成这些动作。以往文献表明，模式分类能够在无需用户交互的情况下，高精度地实现模式之间的无缝切换。尽管分类准确，但使用每个受试者各自的相关数据进行训练和测试非常耗时。我们认为，通过使用独立于受试者的数据集，即将单个受试者与其他受试者的汇总数据集进行对比测试，可以减少受试者的训练时间，同时仍能实现准确分类。在此，我们展示了一种意图识别系统，该系统使用带有缩放共轭梯度学习算法的人工神经网络（ANN），针对六名单侧下肢截肢者，使用与用户相关和独立的数据集对步态意图进行分类。我们将这些结果与线性判别分析（LDA）分类器的结果进行比较。结果发现，对于所有与用户相关的步型以及独立于用户的数据集的过渡步型，ANN的分类误差均显著低于LDA（P<0.05）。两种类型的分类器都能够快速做出决策；LDA和ANN分别为1.29毫秒和2.83毫秒。这些结果表明，人工神经网络能够在假肢步态预测中提供合适且准确的离线分类。
