Comparative Analysis of Artificial Intelligence on Contactless Human Activity Localization

Ambient computing is getting popular as one of the most substantial technological advances in the future. In the present era, human activity tracking, indoor localization, and healthcare systems are all developing rapidly. Researchers are able to find practical solutions in healthcare facilities that often need to locate humans with the growing affordability and power of Radio Frequency (RF) technology. RF is appealing to monitor human activities in an unobtrusive and remote manner. Channel State Information (CSI) can be used as a contactless method to identify and locate human activity indoors. This paper presents the results of an experiment utilizing Universal Software-Defined Radio Peripherals (USRP) to locate the location of activity. A single subject is observed performing sitting, standing, no activity and leaning forward in six different locations inside a room to collect CSI samples. Additional CSI is collected when the subject walks in both directions within the designated area. Three Machine Learning (ML) classification algorithms were used in the comparison: Random Forest, Extra Trees (ET), and Multilayer Perceptron (MLP). When compared to other ML algorithms, the ET classifier has the best performance, with an average of 95% accuracy