Multi-objective Feature Selection in Remote Health Monitoring Applications

• URL: http://arxiv.org/abs/2401.05538v1
• Date: Wed, 10 Jan 2024 20:27:43 GMT
• Title: Multi-objective Feature Selection in Remote Health Monitoring Applications
• Authors: Le Ngu Nguyen and Constantino \'Alvarez Casado and Manuel Lage Ca\~nellas and Anirban Mukherjee and Nhi Nguyen and Dinesh Babu Jayagopi and Miguel Bordallo L\'opez
• Abstract summary: In some scenarios, an RF signal analysis framework may prioritize the performance of one task over that of others. We employ a multi-objective optimization approach inspired by biological principles to select discriminative features that enhance the accuracy of breathing patterns recognition. We present a contrariwise result to maximize user identification accuracy and minimize the system's capacity for breathing activity recognition.
• Score: 2.219956413333153
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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