In this paper, a novel method for early estimation of a human’s action intention is presented. Human intention, modeled as a goal location associated with a hand motion and eye gaze dynamics, is inferred by fusing information from collected hand motion and gaze motion data. The algorithm, called Human Intention Estimator with Variable Structure (HIEVS), uses two variable structure Interacting Multiple Model (VS-IMM) filters in parallel to process the hand motion and gaze data and generate posterior model probabilities associated with a finite set of action models. The posterior model probabilities from each filter are then fused at the end of each iteration and the current intention is estimated as the model, which has the highest fused posterior model probability. Two model set augmentation (MSA) algorithms are presented to select the active models for each VS-IMM during each iteration. For the hand motion filter, an MSA algorithm which computes the human’s reachable workspace is used. The MSA algorithm for the gaze filter utilizes the human’s visual span to determine the active models. This method allows for accurate early prediction of the human’s intention even when the total model set is large. A real world experiment is performed to validate the proposed method.

在本文中，提出了一种新的方法来早期估计人的行动意图。通过融合来自收集到的手部运动和注视数据的信息，可以推断出被建模为与手部运动和眼睛注视动力学相关的目标位置的人类意图。该算法称为具有可变结构的人类意图估算器（HIEVS），该算法并行使用两个可变结构的交互多模型（VS-IMM）过滤器来处理手部动作和注视数据，并生成与有限一组动作模型相关的后验模型概率。然后，在每个迭代的末尾融合来自每个过滤器的后验模型概率，并将当前意图作为模型进行估计，该模型具有最高的融合后验模型概率。提出了两种模型集增强（MSA）算法，以在每次迭代期间为每个VS-IMM选择活动模型。对于手部动作过滤器，使用了一种MSA算法，该算法可以计算人的可到达工作空间。凝视过滤器的MSA算法利用人类的视觉范围来确定活动模型。即使整个模型集很大，此方法也可以对人的意图进行准确的早期预测。进行了真实世界的实验以验证所提出的方法。即使整个模型集很大，此方法也可以对人的意图进行准确的早期预测。进行了真实世界的实验以验证所提出的方法。即使整个模型集很大，此方法也可以对人的意图进行准确的早期预测。进行了真实世界的实验以验证所提出的方法。