A millimeter-wave radar human fall detection algorithm based on TsFreshStacking
Li Mu, Wang Zhao, Luo Yu
(College of Automation and Information Engineering,Xi′an University of Technology,Xi′an 710048,China)
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 TIIWR6843 millimeterwave 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 timeseries features of human falls are extracted by combining the TsFresh timeseries 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,Knearest 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