Intent Recognition Using Neural Networks and Kalman Filters
Intent Recognition Using Neural Networks and Kalman Filters
复制标题
使用神经网络和卡尔曼滤波器进行意图识别
DOI：
--
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
S. Godsill
中科院分区：
文献类型：
--
作者：
P. Biswas;Gökçen Aslan Aydemir;P. Langdon;S. Godsill
指向任务构成图形用户界面中人机交互的重要组成部分。研究人员试图通过根据指针移动特征先验猜测预期目标来减少总体指向时间。由于用户之间指针移动的可变性以及应用程序和目标特征的多样性，该任务提出了挑战。由于存在可变的交互模式，年龄相关或身体有缺陷的用户使得任务更具挑战性。本文提出了一组新模型，用于考虑有或没有运动障碍的用户来预测预期目标。它还建立了一组评估指标来比较这些模型，并最终讨论这些模型的实用性。总体而言，我们在标准多重干扰任务中实现了超过 63% 的目标预测准确度，而我们的模型可以在用户花费 70% 的总指向时间之前识别出正确的目标，这表明在 63% 的指向任务中，指向时间减少了 30%。
Pointing tasks form a significant part of human-computer interaction in graphical user interfaces. Researchers tried to reduce overall pointing time by guessing the intended target a priori from pointer movement characteristics. The task presents challenges due to variability of pointer movements among users and also diversity of applications and target characteristics. Users with age-related or physical impairment makes the task more challenging due to there variable interaction patterns. This paper proposes a set of new models for predicting intended target considering users with and without motor impairment. It also sets up a set of evaluation metrics to compare those models and finally discusses the utilities of those models. Overall we achieved more than 63% accuracy of target prediction in a standard multiple distractor task while our model can recognize the correct target before the user spent 70% of total pointing time, indicating a 30% reduction of pointing time in 63% pointing tasks.
DOI：
10.1080/10447318.2011.636294
发表时间：
2012-01-01
影响因子：
4.7
作者：
Biswas, Pradipta;Langdon, Patrick
通讯作者：
Langdon, Patrick