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Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study
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Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review

1
Computer Science Department, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
2
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
3
Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
4
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
5
Department of Computing Technology, University of Alicante, P.O. Box 99, E-03080 Alicante, Spain
6
Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2020, 9(1), 192; https://doi.org/10.3390/electronics9010192
Received: 9 December 2019 / Revised: 10 January 2020 / Accepted: 13 January 2020 / Published: 20 January 2020
(This article belongs to the Special Issue Machine Learning Techniques for Assistive Robotics)
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. View Full-Text
Keywords: daily activities recognition; ensemble learning; ensemble classifiers; environments; mobile devices; sensors; systematic review daily activities recognition; ensemble learning; ensemble classifiers; environments; mobile devices; sensors; systematic review
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MDPI and ACS Style

Ferreira, J.M.; Pires, I.M.; Marques, G.; Garcia, N.M.; 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, 192.

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