Department of Computer Science, Universidad de Monterrey, Nuevo León 66238, Mexico.
Department of Techniques and Projects in Engineering and Architecture, Universidad de La Laguna, 38071 Tenerife, Spain.
Sensors (Basel). 2020 Jul 15;20(14):3930. doi: 10.3390/s20143930.
The ever-growing and widespread use of touch, face, full-body, and 3D mid-air gesture recognition sensors in domestic and industrial settings is serving to highlight whether interactive gestures are sufficiently inclusive, and whether or not they can be executed by all users. The purpose of this study was to analyze full-body gestures from the point of view of user experience using the Microsoft Kinect sensor, to identify which gestures are easy for individuals living with Down syndrome. With this information, app developers can satisfy Design for All (DfA) requirements by selecting suitable gestures from existing lists of gesture sets. A set of twenty full-body gestures were analyzed in this study; to do so, the research team developed an application to measure the success/failure rates and execution times of each gesture. The results show that the failure rate for gesture execution is greater than the success rate, and that there is no difference between male and female participants in terms of execution times or the successful execution of gestures. Through this study, we conclude that, in general, people living with Down syndrome are not able to perform certain full-body gestures correctly. This is a direct consequence of limitations resulting from characteristic physical and motor impairments. As a consequence, the Microsoft Kinect sensor cannot identify the gestures. It is important to remember this fact when developing gesture-based on Human Computer Interaction (HCI) applications that use the Kinect sensor as an input device when the apps are going to be used by people who have such disabilities.
在家庭和工业环境中，触摸、面部、全身和 3D 空中手势识别传感器的使用日益增多且广泛，这突显了交互手势是否足够具有包容性，以及所有用户是否都可以执行这些手势。本研究的目的是使用 Microsoft Kinect 传感器从用户体验的角度分析全身手势，以确定哪些手势对于患有唐氏综合征的个体来说容易执行。有了这些信息，应用程序开发人员可以通过从现有的手势集列表中选择合适的手势来满足“通用设计”（DfA）的要求。本研究分析了二十个全身手势；为此，研究团队开发了一个应用程序来测量每个手势的成功/失败率和执行时间。结果表明，手势执行的失败率大于成功率，并且男性和女性参与者在执行时间或手势的成功执行方面没有差异。通过这项研究，我们得出结论，一般来说，患有唐氏综合征的人无法正确执行某些全身手势。这是由于身体和运动障碍导致的限制的直接结果。因此，Microsoft Kinect 传感器无法识别这些手势。当使用 Kinect 传感器作为输入设备开发基于手势的人机交互（HCI）应用程序时，如果应用程序将被具有此类残疾的人使用，这一点非常重要。
