<br>人类机器人交互中基于凝视的自然抓取意图识别,IEEE Journal of Biomedical and Health Informatics

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人类机器人交互中基于凝视的自然抓取意图识别
IEEE Journal of Biomedical and Health Informatics ( IF 6.7 ) Pub Date : 1-20-2023 , DOI: 10.1109/jbhi.2023.3238406
Bo Yang ₁ , Jian Huang ₁ , Xinxing Chen ₂ , Xiaolong Li ₁ , Yasuhisa Hasegawa ₃

Affiliation

目的：虽然神经科学研究已经建立了视觉和意图之间的联系，但缺乏针对意图识别的注视数据特征的研究。大多数现有的基于凝视的意图识别方法都是基于有意的长期注视，并且准确性不足。为了解决先前研究中特征缺乏和准确性不足的问题，本研究的主要目标是抑制人类注视数据中的噪声并提取用于识别抓取意图的有用特征。方法：我们进行注视运动评估实验来研究注视运动的特征。基于这些发现，提出了目标吸引注视运动模型（TAGMM）作为注视运动的定量描述。卡尔曼滤波器（KF）用于减少基于 TAGMM 的注视数据中的噪声。我们进行基于注视的自然抓取意图识别评估实验来收集受试者的注视数据。描述注视点分散（$f_{var}$）、注视点移动（$f_{gm}$）、头部移动（$f_{hm}$）以及注视点到物体的距离（$然后提出 f_{d_{j}}$) 来识别主体的抓取意图。利用所提出的特征，我们使用各种分类器进行意图识别实验，并将结果与不同的方法进行比较。结果：统计分析表明，所提出的特征在不同意图之间存在显着差异，提供了利用这些特征来识别掌握意图的可能性。我们展示了利用 TAGMM 的意图识别性能以及在受试者内和跨受试者实验中提出的特征。结果表明，与基于注视的方法相比，所提出的方法可以识别意图，准确率提高了 44.26%（受试者内）和 30.67%（跨受试者）。与基于注视的方法（约 1 秒）相比，所提出的方法识别意图的时间（34.87 毫秒）也更少。结论：这项工作介绍了一种用于建模注视运动的新型 TAGMM 以及用于识别抓取意图的各种实用功能。实验证实了我们方法的有效性。意义：所提出的 TAGMM 能够对注视运动进行建模，并可用于处理注视数据，并且所提出的特征可以揭示用户的意图。这些结果有助于基于凝视的人机交互的发展。

"点击查看英文标题和摘要"

Natural Grasp Intention Recognition Based on Gaze in Human鈥揜obot Interaction

Objective: While neuroscience research has established a link between vision and intention, studies on gaze data features for intention recognition are absent. The majority of existing gaze-based intention recognition approaches are based on deliberate long-term fixation and suffer from insufficient accuracy. In order to address the lack of features and insufficient accuracy in previous studies, the primary objective of this study is to suppress noise from human gaze data and extract useful features for recognizing grasp intention. Methods: We conduct gaze movement evaluation experiments to investigate the characteristics of gaze motion. The target-attracted gaze movement model (TAGMM) is proposed as a quantitative description of gaze movement based on the findings. A Kalman filter (KF) is used to reduce the noise in the gaze data based on TAGMM. We conduct gaze-based natural grasp intention recognition evaluation experiments to collect the subject's gaze data. Four types of features describing gaze point dispersion ($f_{var}$), gaze point movement ($f_{gm}$), head movement ($f_{hm}$), and distance from the gaze points to objects ($f_{d_{j}}$) are then proposed to recognize the subject's grasp intentions. With the proposed features, we perform intention recognition experiments, employing various classifiers, and the results are compared with different methods. Results: The statistical analysis reveals that the proposed features differ significantly across intentions, offering the possibility of employing these features to recognize grasp intentions. We demonstrated the intention recognition performance utilizing the TAGMM and the proposed features in within-subject and cross-subject experiments. The results indicate that the proposed method can recognize the intention with accuracy improvements of 44.26% (within-subject) and 30.67% (cross-subject) over the fixation-based method. The proposed method also consumes less time (34.87 ms) to recognize the intention than the fixation-based method (about 1 s). Conclusion: This work introduces a novel TAGMM for modeling gaze movement and a variety of practical features for recognizing grasp intentions. Experiments confirm the effectiveness of our approach. Significance: The proposed TAGMM is capable of modeling gaze movements and can be utilized to process gaze data, and the proposed features can reveal the user's intentions. These results contribute to the development of gaze-based human-robot interaction.

更新日期：2024-08-28

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