The authors consider radar classifications of activities of daily living, which can prove beneficial in fall detection, analysis of daily routines, and discerning physical and cognitive human conditions. They focus on contiguous motion classifications, which follow and commensurate with the human ethogram of possible motion sequences. Contiguous motions can be closely connected with no clear time gap separations. In the proposed approach, they utilise the Radon transform applied to the radar range-map to detect the translation motion, whereas an energy detector is used to provide the onset and offset times of in-place motions, such as sitting down and standing up. It is shown that motion classifications give different results when performed forward and backward in time. The number of classes, thereby classification rates, considered by a classifier, is made varying depending on the current motion state and the possible transitioning activities in and out of the state. Two different examples are given to delineate the performance of the proposed approach under typical sequences of human motions.

中文翻译：

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连续和连续人类总运动活动的雷达分类

作者考虑了雷达对日常生活活动的分类，这可以证明对跌倒检测，日常生活分析以及辨别人体和认知人体状况有帮助。他们专注于连续的运动分类，该分类遵循并与人类可能的运动序列的人脑图相称。连续运动可以紧密连接，没有明确的时间间隔分隔。在提出的方法中，他们利用应用于雷达距离图的Radon变换来检测平移运动，而能量检测器用于提供就位运动（如坐下和站起）的开始和偏移时间。结果表明，运动分类在时间上向前和向后执行时会给出不同的结果。类的数量，从而分类率，根据当前运动状态以及状态进出状态可能发生的变化，使分类器所考虑的值变化。给出了两个不同的例子来描述所提出的方法在典型的人体运动序列下的性能。