中圖分類號(hào)：TN957.51
文獻(xiàn)標(biāo)識(shí)碼：A
DOI:10.19358/j.issn.2097-1788.2023.06.012
引用格式：李牧,，王昭，駱宇.基于TsFresh-Stacking的毫米波雷達(dá)人體跌倒檢測(cè)方法［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2023,，42（6）：71-78.

A millimeter-wave radar human fall detection algorithm based on TsFreshStacking

Abstract： Aiming at the problems of less spatial information in radar spectrum,low accuracy and poor stability of human fall behavior recognition by millimeter wave radar using a single machine learning algorithm, a fall recognition method combining TsFresh feature extraction and Stacking ensemble learning is proposed using time series data of human space radar point cloud. Firstly, the TIIWR6843 millimeterwave radar is used to collect the time series data of human motion tracking corresponding to human movements,and a data set containing information of different ages,heights,weights,and fall patterns is constructed.Secondly,the key timeseries features of human falls are extracted by combining the TsFresh timeseries feature extraction tool and the feature importance based on the random forest model.Finally,a Stacking ensemble learning method is proposed,which integrates random forest,support vector machine,Knearest neighbor algorithm,XGBoost and CatBoost 5 unit machine learning models.The results show that,compared with the typical single machine learning algorithm,the Stacking ensemble learning algorithm has obvious performance improvement,and can effectively improve the accuracy and generalization of human fall behavior recognition.

Key words : mmWave radar; machine learning; human fall; TsFresh; ensemble learning algorithm