A Deep-Learning Based Real-Time Prediction of Seated Postural Limits and Its Application in Trunk Rehabilitation.
A Deep-Learning Based Real-Time Prediction of Seated Postural Limits and Its Application in Trunk Rehabilitation.
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DOI：
10.1109/tnsre.2022.3221308
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发表时间：
2023
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坐姿限制定义了一个区域的边界，使得对于在该边界之外进行的任何偏移，在没有额外的外部支撑的情况下，受试者不能将躯干返回到中立位置。坐姿限制可作为躯干支撑训练器 (TruST) 为躯干提供辅助支撑的参考。然而，坐姿限制的固定边界表示不足以捕捉训练期间动态变化的坐姿限制。在这项研究中，我们通过分配一个跟踪坐姿任务期间姿势目标方向和躯干运动幅度的向量，提出了躯干中心动态边界的概念模型。我们对 20 名健康受试者进行了实验。结果支持我们的假设，即基于动态边界表示的按需辅助力控制器的 TruST 干预可以比固定边界表示实现更显着的坐姿控制改进。本文的第二个贡献是我们提供了一种将深度学习嵌入到 TruST 实时控制器设计中的有效方法。我们编制了目前文献中最大的 3D 躯干运动数据集。我们设计了一个能够解决门控回归问题的损失函数。我们为探索研究提出了一个新颖的深度学习路线图。按照路线图，我们开发了深度学习架构，修改了广泛使用的Inception模块，然后获得了能够实时准确预测动态边界的深度学习模型。我们相信这种方法可以扩展到其他康复机器人，以设计智能动态基于边界的辅助控制器。
Seated postural limit defines the boundary of a region such that for any excursions made outside this boundary a subject cannot return the trunk to the neutral position without additional external support. The seated postural limits can be used as a reference to provide assistive support to the torso by the Trunk Support Trainer (TruST). However, fixed boundary representations of seated postural limits are inadequate to capture dynamically changing seated postural limits during training. In this study, we propose a conceptual model of dynamic boundary of the trunk center by assigning a vector that tracks the postural-goal direction and trunk movement amplitude during a sitting task. We experimented with 20 healthy subjects. The results support our hypothesis that TruST intervention with an assist-as-needed force controller based on dynamic boundary representation could achieve more significant sitting postural control improvements than a fixed boundary representation. The second contribution of this paper is that we provide an effective approach to embed deep learning into TruST’s real-time controller design. We have compiled a 3D trunk movement dataset which is currently the largest in the literature. We designed a loss function capable of solving the gate-controlled regression problem. We have proposed a novel deep-learning roadmap for the exploration study. Following the roadmap, we developed a deep learning architecture, modified the widely used Inception module, and then obtained a deep learning model capable of accurately predicting the dynamic boundary in real-time. We believe that this approach can be extended to other rehabilitation robots towards designing intelligent dynamic boundary-based assist-as-needed controllers.