Hierarchical User Status Classification for Imbalanced Biometric Data Class

With the proliferation of Internet of Things technologies, health care services that target a household equipped with IoT devices are widely emerging. In the meantime, the number of global single households is expected to rapidly grow. Contactless radar-based sensors are recently investigated as a convenient and practical means to collect biometric data of subjects in single households. In this paper, biometric data collected by contactless radar-based sensors installed in single households of the elderly under uncontrolled environments are analyzed, and a deep learning-based classification model is proposed that estimates a user’s status in one of the predefined classes. In particular, the issue of the imbalance class sizes in the generated dataset is managed by reorganizing the classes into a hierarchical structure and designing the architecture for a deep learning-based status classification model. The experimental results verify that the proposed classification model has a noticeable impact in mitigating the issue of imbalanced class sizes as it enhances the classification accuracy of the individual class by up to 65% while improving the overall status classification accuracy by 6%.