Abstract

We explore the possibility of leveraging radar-based sensing systems to analyze vital signs for classification, user identification, and regression tasks. Specifically, we extract time-domain and frequency-domain features from distance, respiration, and pulse signals obtained by filtering radio-frequency signals. Our Random Forest classification models are trained on these features to recognize scenarios in which the radar data were collected, categorize individuals into age groups, and classify human activities. For classification, we achieved up to 94.7% of accuracy when distinguishing apnea and normal breathing in the lying position. We then show the feasibility of identifying individuals in a small group using vital signs, which can support model fine-tuning with data acquired from new users. Furthermore, we used a Random Forest regression model to estimate the Body Mass Index, height, and weight of subjects. These classification, identification, and regression models benefit smart systems that can simultaneously identify users, recognize their behaviours, and extract their vital signs from radar sensors.