基于多特征学习融合级联分类的跌倒识别. (Chinese)

To solve the problem that the training errors of different fall methods cannot converge sufficiently, which leads to poor classification effect, a Cascade Fall Recognition (CFR) scheme for cascade classification is proposed based on the extraction of three-dimensional features of acceleration, angular velocity and combined acceleration of wearable devices. At the same time, to reduce the complexity of Cascade serial operation, a low-complexity Cascade Fall Recognition (LCFR) scheme based on dimensionality reduction of empirical features is further proposed. Finally, for SisFall dataset, a single multi-classifier scheme, CFR scheme and LCFR scheme were used to perform four classifications (daily behavior, forward fall, lateral fall and backward fall), and the F1-Score and training time complexity were compared. The experimental results show that the F1-Score of CFR scheme is 98.56% in the four-category fall task classification. On the premise of approaching lossless F1-score, the training time complexity of LCFR scheme is 61.11% lower than that of CFR scheme, and the F1-score of scheme is higher than that of similar fall recognition schemes. [ABSTRACT FROM AUTHOR]

为解决不同跌倒方式的训练误差无法充分收敛而导致分类效果欠佳的问题, 在提取穿戴式设备加速度、角速度及合加速度三维特征的基础上, 提出了一种级联分类的跌倒识别(Cascade Fall Recognition, CFR)方案; 同时为降低级联串行运算的复杂度, 进一步提出了基于经验特征维度降维的低复杂度级联分类跌倒识别(Low-complexity Cascade Fall Recognition, LCFR)方案. 最后, 面向SisFall数据集, 分别使用单个多分类器方案、CFR方案和LCFR方案进行四分类任务(日常行为, 向前跌倒, 横向跌倒, 向后跌倒), 对F1-Score和训练时间复杂度进行对比. 实验结果表明: CFR方案在四分类跌倒任务时的F1-Score达98.56%; 在接近无损F1-Score的前提下, LCFR方案的训练时间复杂度比CFR方案降低了61.11%, 且该方案的F1-Score高于同类跌倒识别方案. [ABSTRACT FROM AUTHOR]

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