This paper presents accurate grasp prediction algorithms that can be used for naturalistic, synergistic control of exoskeleton gloves with minimal user input. Recent research in exoskeleton systems has focused mainly on the development of novel soft or hard mechanical designs and actuation systems for rehabilitative and assistive applications. On the other hand, estimating user intent for intelligent grasp assistance is a problem that has remained largely unaddressed. As demonstrated by existing studies, the complex motions of human hand can be mapped to a latent space, thereby reducing perceived noise in individual joint angles as well as the number of variables upon which the prediction must be performed. To this extent, we present two latent space grasp prediction algorithms for intelligent exoskeleton glove control. The first presented algorithm is based on a linear regression to determine the slope and prediction horizon. The second algorithm is based on a Gaussian process trajectory matching where the trajectory of the grasping motion is probabilistically compared to existing data in order to form a prediction. Both algorithms were tested on published motion data collected from healthy subjects. In addition, the experimental validation of the algorithms was done using the RML glove (Robotics and Mechatronics Lab), which yielded similar prediction accuracy as compared to the simulation results. The proposed prediction algorithm can act as the backbone for a shifting authority controller that simultaneously amplifies the user's motion while guiding them toward their desired grasp. Preliminary work in this direction is also described in the paper, with directions for future research.

中文翻译：

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掌握对自然外骨骼手套控制的预测

本文提出了准确的抓取预测算法，可用于以最少的用户输入对外骨骼手套进行自然、协同控制。最近外骨骼系统的研究主要集中在开发用于康复和辅助应用的新型软或硬机械设计和驱动系统。另一方面，估计智能抓取辅助的用户意图是一个在很大程度上仍未解决的问题。现有研究表明，人手的复杂运动可以映射到潜在空间，从而减少各个关节角度的感知噪声以及必须执行预测的变量数量。为此，我们提出了两种用于智能外骨骼手套控制的潜在空间抓取预测算法。第一个提出的算法基于线性回归来确定斜率和预测范围。第二种算法基于高斯过程轨迹匹配，其中抓取运动的轨迹与现有数据进行概率比较以形成预测。两种算法都在从健康受试者收集的已发布运动数据上进行了测试。此外，算法的实验验证是使用 RML 手套（机器人和机电一体化实验室）完成的，与模拟结果相比，它产生了相似的预测精度。所提出的预测算法可以作为移动权限控制器的骨干，同时放大用户的运动，同时引导他们朝着他们想要的方向前进。论文中也描述了这方面的初步工作，