Multi-objective Feature Selection in Remote Health Monitoring Applications
This work addresses privacy and performance trade-offs in remote health monitoring applications, though it appears incremental as it applies known optimization methods to a specific scenario.
The paper tackled the problem of balancing performance between breathing pattern recognition and user identification in RF-based health monitoring by using a multi-objective optimization approach, achieving a substantial divergence in accuracy between these tasks on a dataset of 50 subjects with four breathing patterns.
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.