Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review
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Título: | Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review |
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Autor/es: | Ferreira, José M. | Pires, Ivan Miguel | Marques, Gonçalo | Garcia, Nuno M. | Zdravevski, Eftim | Lameski, Petre | Flórez-Revuelta, Francisco | Spinsante, Susanna |
Grupo/s de investigación o GITE: | Informática Industrial y Redes de Computadores |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Daily activities recognition | Ensemble learning | Ensemble classifiers | Environments | Mobile devices | Sensors | Systematic review |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 20-ene-2020 |
Editor: | MDPI |
Cita bibliográfica: | Ferreira JM, Pires IM, Marques G, Garcia NM, Zdravevski E, Lameski P, Flórez-Revuelta F, Spinsante S. Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review. Electronics. 2020; 9(1):192. doi:10.3390/electronics9010192 |
Resumen: | Using the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method. |
Patrocinador/es: | This work is funded by FCT/MEC through national funds and when applicable co-funded by FEDER—PT2020 partnership agreement under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MEC através de fundos nacionais e quando aplicável cofinanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 no âmbito do projeto UIDB/EEA/50008/2020). This article is based upon work from COST Action IC1303-AAPELE-Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226-SHELD-ON-Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information in www.cost.eu. |
URI: | http://hdl.handle.net/10045/101750 |
ISSN: | 2079-9292 |
DOI: | 10.3390/electronics9010192 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/electronics9010192 |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas INV - AmI4AHA - Artículos de Revistas |
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