Recurrence Quantification Analysis for Human Activity Recognition

Human Activity Recognition (HAR) is a central unit to understand and predict human behavior. HAR has been used to estimate the levels of a sedentary, monitor lifestyle habits, track the levels of people's health, or build a recommendation system. Many researchers have utilized the inertial measurement unit as an input tool to explore the HAR land. The recurrence plot (RP) technique recently has its applications diverse in various areas. From the recurrence plot, a machine-auto or hand-crafted approach can be used to extract feature vectors. While the machine-auto based approach has been reported in the literature, the latter hand-crafted based method has not. For that reason, this paper evaluated and demonstrated the feasibility of utilizing Recurrence Quantification Analysis (RQA), which was a typical hand-crafted method from RP, to classify human activities. A Linear Discriminant Analysis classifier yielded a 95.08% accuracy, which belonged in the top accuracy reported in the literature. Compare to the machine-auto or end-to-end approach, RQA is a far less complicated and more lean system that should be further analyzed in a HAR application.