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Performance evaluation of different age groups for gestural interaction: a case study with Microsoft Kinect and Leap Motion

机译:不同年龄群的特性依次进行性能评估 - 以微软kinect和飞跃运动为例

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摘要

With the thriving of different natural interaction paradigms-such as gesture-based interfaces-it becomes important to understand how these novel interfaces can influence users' performance when it comes to their age. Recent advances made in human-computer interaction allow us to manipulate digital contents more intuitively; however, no work has yet been reported that systematically evaluates how gestural interfaces may influence the performance of different user groups. Different optical sensors, which allow human body acquisition with reliable accuracy, have been released, and with the appearance of such controllers for gesture recognition, it becomes important to understand if different age-related groups display similar performance levels concerning gestural interaction, or, on the other hand, if specific sensors could induce better results than others when dealing with users of different age brackets. In this article, we compare two gesture-sensing devices (Microsoft Kinect and Leap Motion) using the Fitts' law model to evaluate target acquisition performance, with relation to three user groups: children, young adults and older adults. This case study involved 60 participants that were asked to perform a simple continuous selection task as quickly and accurately as possible using one of the devices for gestural recognition. Indeed, performance results showed statistically significant differences among the age groups in the selection task accomplished. However, when considering the users' performance with regard to both input devices compared side by side, there were no significant differences in each group of users. We believe this situation could imply that the device itself might not have influenced the users' performance, but actually the users' age might. The participants feedback was interesting on account of their behaviors and preferences: Although there are no significant differences in performance, there could be when it comes to user preference.
机译:随着不同的自然交互范式范式 - 如手势的界面 - 了解这些新颖的界面如何在涉及到年龄时会影响用户的性能。最近在人机互动中取得的进展允许我们更直观地操纵数字内容;但是,尚未报告任何工作,系统评估了识别界面如何影响不同用户组的性能。允许具有可靠精度的人体采集的不同光学传感器,并且随着这种控制器的出现进行手势识别,可以了解不同年龄相关的群体是否显示有关手势相互作用的类似性能水平,或者另一方面,如果特定传感器可以在处理不同年龄括号的用户时比其他传感器诱导更好的结果。在本文中,我们使用Fitts的法律模型比较两个手势传感设备(Microsoft Kinect和Leap Motion)来评估目标采集性能,与三个用户组有关:儿童,年轻成人和老年人。本案例研究涉及60名参与者,这些参与者被要求尽可能快速准确地使用其中一个用于故障识别的设备来执行简单的连续选择任务。实际上,表现结果表明,选择任务中的年龄组之间存在统计学意义。但是,在将用户对两个输入设备相比相比,在相比之下,每组用户都没有显着差异。我们相信这种情况可能意味着设备本身可能不会影响用户的性能,但实际上用户的年龄可能。参与者的反馈是因为他们的行为和偏好有趣

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