Multi-Sensor Fusion for Activity Recognition—A Survey
In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activiti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/17/3808 |
| _version_ | 1811303399358988288 |
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| author | Antonio A. Aguileta Ramon F. Brena Oscar Mayora Erik Molino-Minero-Re Luis A. Trejo |
| author_facet | Antonio A. Aguileta Ramon F. Brena Oscar Mayora Erik Molino-Minero-Re Luis A. Trejo |
| author_sort | Antonio A. Aguileta |
| collection | DOAJ |
| description | In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area. |
| first_indexed | 2024-04-13T07:47:14Z |
| format | Article |
| id | doaj.art-c49b89d1b36c4494957572d571348360 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:47:14Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c49b89d1b36c4494957572d5713483602022-12-22T02:55:39ZengMDPI AGSensors1424-82202019-09-011917380810.3390/s19173808s19173808Multi-Sensor Fusion for Activity Recognition—A SurveyAntonio A. Aguileta0Ramon F. Brena1Oscar Mayora2Erik Molino-Minero-Re3Luis A. Trejo4Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, MexicoTecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, MexicoFandazione Bruno Kessler Foundation, 38123 Trento, ItalyInstituto 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, Yucatan 97302, MexicoTecnologico de Monterrey, School of Engineering and Sciences, Carretera al Lago de Guadalupe Km. 3.5, Atizapán de Zaragoza 52926, MexicoIn Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.https://www.mdpi.com/1424-8220/19/17/3808multi-sensor fusionactivity recognitionsurvey |
| spellingShingle | Antonio A. Aguileta Ramon F. Brena Oscar Mayora Erik Molino-Minero-Re Luis A. Trejo Multi-Sensor Fusion for Activity Recognition—A Survey Sensors multi-sensor fusion activity recognition survey |
| title | Multi-Sensor Fusion for Activity Recognition—A Survey |
| title_full | Multi-Sensor Fusion for Activity Recognition—A Survey |
| title_fullStr | Multi-Sensor Fusion for Activity Recognition—A Survey |
| title_full_unstemmed | Multi-Sensor Fusion for Activity Recognition—A Survey |
| title_short | Multi-Sensor Fusion for Activity Recognition—A Survey |
| title_sort | multi sensor fusion for activity recognition a survey |
| topic | multi-sensor fusion activity recognition survey |
| url | https://www.mdpi.com/1424-8220/19/17/3808 |
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