Using affective human-machine interface to increase the operation performance in virtual construction crane training system: A novel approach
Using affective human-machine interface to increase the operation performance in virtual construction crane training system: A novel approach
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DOI：
10.1016/j.autcon.2010.10.005
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发表时间：
2011-05
影响因子：
10.3
通讯作者：
I. Rezazadeh;Xiangyu Wang;M. Firoozabadi;M. Golpayegani
中科院分区：
文献类型：
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作者：
I. Rezazadeh;Xiangyu Wang;M. Firoozabadi;M. Golpayegani
在建筑行业，研究人员已经取得了一些进展，设计和实施使用VR技术及其衍生产品，如增强现实和混合现实的任务训练环境。虽然，这些发展已经在应用层面上得到了很好的认可，但对虚拟训练系统至关重要的是有效和可靠的测量特定技能的训练表现和长期处理实验。众所周知，运动技能不能直接测量，而只能通过观察行为或性能测量来推断。测量绩效的典型方法是通过测量任务完成时间和准确性，但也可以通过一些其他因素的间接测量来支持。在本文中，一个虚拟的起重机培训系统已经开发出来，可以使用从面部表情提取的控制命令进行控制，并能够解除负载/材料在虚拟建筑工地。然后，我们将情感计算的概念融入到传统的VR训练平台，使用人类的前额生物电信号测量认知负荷和满意度。通过采用情感的措施和我们的新的控制方案，所设计的界面可以适应用户的情感状态，在实时性能。这种适应性强的用户界面方法有助于受训者科普长期绩效培训，获得更多专业知识，并将学习更有效地转移到其他操作环境。本文提出了情感控制的具体方法。结果和未来的应用所提出的方法为残疾人用户，特别是从颈部以下进行了讨论。
In the construction industry, some progress have been achieved by researchers to design and implement environments for task training using VR technology and its derivatives such as Augmented and Mixed Reality. Although, these developments have been well recognized at the application level, however crucial to the virtual training system is the effective and reliable measurement of training performance of the particular skill and handling the experiment for long-run. It is known that motor skills cannot be measured directly, but only inferred by observing behaviour or performance measures. The typical way of measuring performance is through measuring task completion time and accuracy, but can be supported by indirect measurement of some other factors. In this paper, a virtual crane training system has been developed which can be controlled using control commands extracted from facial gestures and is capable to lift up loads/materials in the virtual construction sites. Then, we integrate affective computing concept into the conventional VR training platform for measuring the cognitive load and level of satisfaction during performance using human's forehead bioelectric-signals. By employing the affective measures and our novel control scheme, the designed interface could be adapted to user's affective status during the performance in real-time. This adaptable user interface approach helps the trainee to cope with the training for long-run performance, leads to gaining more expertise and provides more effective transfer of learning to other operation environments. The detailed methodology of the affective control is presented in the paper. The results and future applications of the proposed method for disabled users, especially from neck down are discussed.