Multi-objective Feature Selection in Remote Health Monitoring Applications
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914637309542400 |
|---|---|
| author | Nguyen, Le Ngu Casado, Constantino Álvarez Cañellas, Manuel Lage Mukherjee, Anirban Nguyen, Nhi Jayagopi, Dinesh Babu López, Miguel Bordallo |
| author_facet | Nguyen, Le Ngu Casado, Constantino Álvarez Cañellas, Manuel Lage Mukherjee, Anirban Nguyen, Nhi Jayagopi, Dinesh Babu López, Miguel Bordallo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05538 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-objective Feature Selection in Remote Health Monitoring Applications Nguyen, Le Ngu Casado, Constantino Álvarez Cañellas, Manuel Lage Mukherjee, Anirban Nguyen, Nhi Jayagopi, Dinesh Babu López, Miguel Bordallo Machine Learning 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. |
| title | Multi-objective Feature Selection in Remote Health Monitoring Applications |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.05538 |