**5. Conclusions**

This review presents studies available in the literature that use the AdaBoost method for the recognition of daily activities and environments. Thirteen studies were analysed, and the main findings are summarised as follows:


This review also highlights the use of smartphones and other mobile devices as they should have a particular purpose because of limited battery life and processing capabilities. First, the authors excluded studies that are not focused on the recognition of daily activities end environments with the AdaBoost method. Secondly, the studies that do not use sensors available on mobile devices were excluded. We excluded several studies after analysis of the abstracts and full-text of the papers. Another reason for exclusion was the language of the study, excluding the studies that were not written in English. With the features collected, the AdaBoost method allows recognition with an accuracy higher than 80%.

As future work, the implementation of the AdaBoost method in the framework for the recognition of daily activities and environments; it will be used to recognize seven daily activities and nine environments.

**Author Contributions:** Conceptualization, methodology, software, validation, formal analysis, investigation,writing—original draft preparation, writing—review and editing: J.M.F., I.M.P., G.M., N.M.G., E.Z., P.L., F.F.-R. and S.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is funded by FCT/MEC through national funds and when applicable co-funded by FEDER—PT2020 partnership agreemen<sup>t</sup> 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*).

**Acknowledgments:** This work is funded by FCT/MEC through national funds and when applicable co-funded by FEDER—PT2020 partnership agreemen<sup>t</sup> 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.

**Conflicts of Interest:** The authors declare no conflict of interest.
