Choosing the Best Sensor Fusion Method: A Machine-Learning Approach
Multi-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this ar...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/8/2350 |
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| author | Ramon F. Brena Antonio A. Aguileta Luis A. Trejo Erik Molino-Minero-Re Oscar Mayora |
| author_facet | Ramon F. Brena Antonio A. Aguileta Luis A. Trejo Erik Molino-Minero-Re Oscar Mayora |
| author_sort | Ramon F. Brena |
| collection | DOAJ |
| description | Multi-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this area has made progress, and the combined use of several sensors has been so successful that many authors proposed variants of fusion methods, to the point that it is now hard to tell which of them is the best for a given set of sensors and a given application context. To address the issue of choosing an adequate fusion method, we recently proposed a machine-learning data-driven approach able to predict the best merging strategy. This approach uses a meta-data set with the Statistical signatures extracted from data sets of a particular domain, from which we train a prediction model. However, the mentioned work is restricted to the recognition of human activities. In this paper, we propose to extend our previous work to other very different contexts, such as gas detection and grammatical face expression identification, in order to test its generality. The extensions of the method are presented in this paper. Our experimental results show that our extended model predicts the best fusion method well for a given data set, making us able to claim a broad generality for our sensor fusion method. |
| first_indexed | 2024-03-10T20:20:18Z |
| format | Article |
| id | doaj.art-6703e2dcb45249028acf79893d8a1e5f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:20:18Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6703e2dcb45249028acf79893d8a1e5f2023-11-19T22:13:53ZengMDPI AGSensors1424-82202020-04-01208235010.3390/s20082350Choosing the Best Sensor Fusion Method: A Machine-Learning ApproachRamon F. Brena0Antonio A. Aguileta1Luis A. Trejo2Erik Molino-Minero-Re3Oscar Mayora4Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, MexicoTecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, MexicoTecnologico de Monterrey, School of Engineering and Sciences, Carretera al Lago de Guadalupe Km. 3.5, Atizapán de Zaragoza 52926, MexicoInstituto de Investigaciones en Matemáticas Aplicadas y en Sistemas—Sede Mérida, Unidad Académica de Ciencias y Tecnología de la UNAM en Yucatán, Universidad Nacional Autónoma de México, Sierra Papacal 97302, MexicoFandazione Bruno Kessler Foundation, 38123 Trento, ItalyMulti-sensor fusion refers to methods used for combining information coming from several sensors (in some cases, different ones) with the aim to make one sensor compensate for the weaknesses of others or to improve the overall accuracy or the reliability of a decision-making process. Indeed, this area has made progress, and the combined use of several sensors has been so successful that many authors proposed variants of fusion methods, to the point that it is now hard to tell which of them is the best for a given set of sensors and a given application context. To address the issue of choosing an adequate fusion method, we recently proposed a machine-learning data-driven approach able to predict the best merging strategy. This approach uses a meta-data set with the Statistical signatures extracted from data sets of a particular domain, from which we train a prediction model. However, the mentioned work is restricted to the recognition of human activities. In this paper, we propose to extend our previous work to other very different contexts, such as gas detection and grammatical face expression identification, in order to test its generality. The extensions of the method are presented in this paper. Our experimental results show that our extended model predicts the best fusion method well for a given data set, making us able to claim a broad generality for our sensor fusion method.https://www.mdpi.com/1424-8220/20/8/2350optimaldata fusionmeta-datasensor fusion |
| spellingShingle | Ramon F. Brena Antonio A. Aguileta Luis A. Trejo Erik Molino-Minero-Re Oscar Mayora Choosing the Best Sensor Fusion Method: A Machine-Learning Approach Sensors optimal data fusion meta-data sensor fusion |
| title | Choosing the Best Sensor Fusion Method: A Machine-Learning Approach |
| title_full | Choosing the Best Sensor Fusion Method: A Machine-Learning Approach |
| title_fullStr | Choosing the Best Sensor Fusion Method: A Machine-Learning Approach |
| title_full_unstemmed | Choosing the Best Sensor Fusion Method: A Machine-Learning Approach |
| title_short | Choosing the Best Sensor Fusion Method: A Machine-Learning Approach |
| title_sort | choosing the best sensor fusion method a machine learning approach |
| topic | optimal data fusion meta-data sensor fusion |
| url | https://www.mdpi.com/1424-8220/20/8/2350 |
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