Abstract：
Considering that the population is aging rapidly, the demand for technology for aging-at-home, which can provide reliable, unobtrusive monitoring of human activity, is expected to expand. This research focuses on improving the solution of the posture detection problem, which is a part of fall detection system. Fall detection, using depth maps obtained by the Microsoft Kinect sensor, is a two-stage method. We concentrate on the first stage of the system, that is, pose recognition from a depth map. For lying pose detection, a new hybrid FRSystem is proposed. In the system, two rule sets are investigated, the first one created based on a domain knowledge and the second induced based on the rough set theory. Additionally, two inference aggregation approaches are considered with and without the knowledge measure. The results indicate that the new axiomatic definition of knowledge measures, which we propose has a positive impact on the effectiveness of inference and the rule induction method reducing the number of rules in a set maintains it.
摘要：
考虑到人口正在迅速老龄化，对家庭老化技术的需求，它可以提供可靠的，对人类活动的不显眼的监控，预计将扩大。本研究的重点是改进姿态检测问题的解决方案，这是跌倒检测系统的一部分。跌倒检测,使用MicrosoftKinect传感器获取的深度图，是一个两阶段的方法。我们专注于系统的第一阶段，也就是说,从深度图的姿态识别。对于躺着的姿势检测，提出了一种新的混合FRSystem。在系统中,调查了两个规则集，第一个是基于领域知识创建的，第二个是基于粗糙集理论诱导的。此外,考虑了有和没有知识度量的两种推理聚合方法。结果表明，知识测度的新公理定义，我们提出的建议对推理的有效性有积极的影响，减少集合中规则数量的规则归纳法保持了它。
