Kazemimoghadam Mahdieh, Fey Nicholas P
Department of Bioengineering, The University of Texas at Dallas, Richardson, TX, United States.
Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX, United States.
Front Bioeng Biotechnol. 2021 Apr 22;9:628050. doi: 10.3389/fbioe.2021.628050. eCollection 2021.
Intent recognition in lower-extremity assistive devices (e.g., prostheses and exoskeletons) is typically limited to either recognition of steady-state locomotion or changes of terrain (e.g., level ground to stair) occurring in a straight-line path and under anticipated condition. Stability is highly affected during non-steady changes of direction such as cuts especially when they are unanticipated, posing high risk of fall-related injuries. Here, we studied the influence of changes of direction and user anticipation on task recognition, and accordingly introduced classification schemes accommodating such effects.
A linear discriminant analysis (LDA) classifier continuously classified straight-line walking, sidestep/crossover cuts (single transitions), and cuts-to-stair locomotion (mixed transitions) performed under varied task anticipatory conditions. Training paradigms with varying levels of anticipated/unanticipated exposures and analysis windows of size 100-600 ms were examined.
More accurate classification of anticipated relative to unanticipated tasks was observed. Including bouts of target task in the training data was necessary to improve generalization to unanticipated locomotion. Only up to two bouts of target task were sufficient to reduce errors to <20% in unanticipated mixed transitions, whereas, in single transitions and straight walking, substantial unanticipated information (i.e., five bouts) was necessary to achieve similar outcomes. Window size modifications did not have a significant influence on classification performance.
Adjusting the training paradigm helps to achieve classification schemes capable of adapting to changes of direction and task anticipatory state.
The findings could provide insight into developing classification schemes that can adapt to changes of direction and user anticipation. They could inform intent recognition strategies for controlling lower-limb assistive to robustly handle "unknown" circumstances, and thus deliver increased level of reliability and safety.
下肢辅助设备（如假肢和外骨骼）中的意图识别通常仅限于对稳态运动的识别，或在直线路径和预期条件下发生的地形变化（如平地到楼梯）的识别。在非稳态方向变化（如转弯）尤其是意外转弯时，稳定性会受到很大影响，存在与跌倒相关伤害的高风险。在此，我们研究了方向变化和用户预期对任务识别的影响，并相应地引入了适应此类影响的分类方案。
线性判别分析（LDA）分类器连续对在不同任务预期条件下执行的直线行走、侧步/交叉转弯（单次转换）和转弯上楼梯运动（混合转换）进行分类。研究了具有不同预期/意外暴露水平的训练范式以及大小为100 - 600毫秒的分析窗口。
观察到与意外任务相比，预期任务的分类更准确。在训练数据中纳入目标任务的片段对于提高对意外运动的泛化能力是必要的。在意外混合转换中，仅多达两个目标任务片段就足以将错误降低到<20%，而在单次转换和直线行走中，需要大量的意外信息（即五个片段）才能达到类似结果。窗口大小的修改对分类性能没有显著影响。
调整训练范式有助于实现能够适应方向变化和任务预期状态的分类方案。
这些发现可为开发能够适应方向变化和用户预期的分类方案提供见解。它们可为控制下肢辅助设备的意图识别策略提供参考，以稳健地处理“未知”情况，从而提高可靠性和安全性。
