The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.

中文翻译：

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基于分数域的老年人跌倒前检测

人口老龄化已成为世界范围内日益严重的问题。每年，由于老年人跌倒造成的伤亡都会带来巨大的社会成本。为了降低老年人跌倒造成的伤害和死亡率以及随之而来的社会成本，必须对老年人进行监控。在这种情况下，跌倒检测已成为国内外许多研究机构和企业的热点。本文提出了一种基于分数域的跌倒预警算法框架，以惯性数据传感器作为运动数据采集设备，通过轴合成和均值滤波对数据进行预处理，并利用分数阶傅里叶变换将所采集的数据从时域进行转换。到分数域。在此基础上，设计了一种多层二分法分类器，给出了每种节点参数的选择方法，构建了性能卓越的预碰撞跌倒检测系统。实验结果表明，本文提出的算法能够以较少的特征保证更好的预警效果和分类精度。