Fast and accurate human intention prediction can significantly advance the performance of assistive devices for patients with limited motor or communication abilities. Among available modalities, eye movement can be valuable for inferring the user's intention, as it can be tracked non-invasively. However, existing limited studies in this domain do not provide the level of accuracy required for the reliable operation of assistive systems. By taking a data-driven approach, this paper presents a new framework that utilizes the spatial and temporal patterns of eye movement along with deep learning to predict the user's intention. In the proposed framework, the spatial patterns of gaze are identified by clustering the gaze points based on their density over displayed images in order to find the regions of interest (ROIs). The temporal patterns of gaze are identified via hidden Markov models (HMMs) to find the transition sequence between ROIs. Transfer learning is utilized to identify the objects of interest in the displayed images. Finally, models are developed to predict the user's intention after completing the task as well as at early stages of the task. The proposed framework is evaluated in an experiment involving predicting intended daily-life activities. Results indicate that an average classification accuracy of 97.42% is achieved, which is considerably higher than existing gaze-based intention prediction studies.

中文翻译：

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通过眼动预测意图的数据驱动框架及其在辅助系统中的应用


快速准确的人类意图预测可以显着提高运动或沟通能力有限患者的辅助设备的性能。在可用的方式中，眼球运动对于推断用户的意图很有价值，因为它可以被非侵入性地跟踪。然而，该领域现有的有限研究并未提供辅助系统可靠运行所需的准确性水平。通过采用数据驱动的方法，本文提出了一个新的框架，利用眼球运动的空间和时间模式以及深度学习来预测用户的意图。在所提出的框架中，通过根据显示图像上的注视点密度对注视点进行聚类来识别注视的空间模式，以找到感兴趣的区域（ROI）。通过隐马尔可夫模型 (HMM) 识别凝视的时间模式，以找到 ROI 之间的转换序列。利用迁移学习来识别显示图像中感兴趣的对象。最后，开发模型来预测用户完成任务后以及任务早期阶段的意图。所提出的框架在涉及预测预期日常生活活动的实验中进行了评估。结果表明，平均分类准确率达到 97.42%，明显高于现有的基于凝视的意图预测研究。