  Radio frequency (RF) signals have facilitated the development of non-contact
human monitoring tasks, such as vital signs measurement, activity recognition,
and user identification. In some specific scenarios, an RF signal analysis
framework may prioritize the performance of one task over that of others. In
response to this requirement, we employ a multi-objective optimization approach
inspired by biological principles to select discriminative features that
enhance the accuracy of breathing patterns recognition while simultaneously
impeding the identification of individual users. This approach is validated
using a novel vital signs dataset consisting of 50 subjects engaged in four
distinct breathing patterns. Our findings indicate a remarkable result: a
substantial divergence in accuracy between breathing recognition and user
identification. As a complementary viewpoint, we present a contrariwise result
to maximize user identification accuracy and minimize the system's capacity for
breathing activity recognition.
