摘要：Human activity recognition (HAR) systems are widely used in our lives, such as healthcare, security, and entertainment. Most of the activity recognition models are tested in the personal mode, and the performance is quite good. However, HAR in the impersonal mode is still a great challenge. In this paper, we propose a two-layer activity sparse grouping (TASG) model, in which the first layer clusters the activities into 2–4 groups roughly and the second layer identifies the specific type of activities. A new feature selection metric inspired by the Fisher criterion is designed to measure the importance of the features. We perform the experiment using the TASG model with SVM, KNN, Random Forest, and RNN, respectively. The experiments are tested on HAPT, MobiAct, and HASC-PAC2016 datasets. The experimental results show that the performance of standard classifiers has been improved while combining the TASG method. The features selected by the proposed metric are more effective than other FS methods.
