Spanias John A, Simon Ann M, Hargrove Levi J
IEEE Int Conf Rehabil Robot. 2017 Jul;2017:1580-1583. doi: 10.1109/ICORR.2017.8009473.
Pattern recognition algorithms have been used to control powered lower limb prostheses because they are capable of identifying the intent of the amputee user and therefore can provide a method for seamlessly transitioning between the different locomotion modes of the prosthesis. However, one major limitation of current algorithms is that they require subject-specific data from the individual user. These data are difficult and time-consuming to collect and consequently these algorithms do not generalize well across users. We have developed an adaptive pattern recognition algorithm that automatically learns new subject-specific data acquired from a novel user during ambulation. We tested this adaptive algorithm with one transfemoral amputee subject walking on a powered knee-ankle prosthesis. Before adaptation, the algorithm, which was initially trained with data from two other transfemoral amputee subjects, made critical errors that prevented continuous ambulation. With adaptation, error rates dropped from 4.21% before adaptation to 1.25% after adaptation, and allowed the novel amputee user to complete all mode transitions. This study demonstrates that adaptation can decrease error rates over time and can allow pattern recognition algorithms to generalize to novel users.
模式识别算法已被用于控制下肢动力假肢，因为它们能够识别截肢者使用者的意图，因此可以提供一种在假肢的不同运动模式之间无缝转换的方法。然而，当前算法的一个主要限制是它们需要来自个体使用者的特定于个体的数据。这些数据收集起来既困难又耗时，因此这些算法在不同使用者之间的通用性不佳。我们开发了一种自适应模式识别算法，该算法能自动学习在行走过程中从新用户那里获取的新的特定于个体的数据。我们用一名经股截肢者受试者在动力膝盖-脚踝假肢上行走来测试这种自适应算法。在进行自适应之前，该算法最初是用来自另外两名经股截肢者受试者的数据进行训练的，出现了严重错误，导致无法持续行走。通过自适应，错误率从自适应前的4.21%降至自适应后的1.25%，并使这位新的截肢者使用者能够完成所有模式转换。这项研究表明，随着时间的推移，自适应可以降低错误率，并能使模式识别算法推广到新用户。
