*Article* **Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study**

**José M. Ferreira 1,†, Ivan Miguel Pires 2,3,\*,†, Gonçalo Marques 2,†, Nuno M. Garcia 2,†, Eftim Zdravevski 4,†, Petre Lameski 4,†, Francisco Flórez-Revuelta 5,† and Susanna Spinsante 6,† and Lina Xu 7,†**


Received: 18 December 2019; Accepted: 14 January 2020; Published: 18 January 2020

**Abstract:** The recognition of Activities of Daily Living (ADL) using the sensors available in off-the-shelf mobile devices with high accuracy is significant for the development of their framework. Previously, a framework that comprehends data acquisition, data processing, data cleaning, feature extraction, data fusion, and data classification was proposed. However, the results may be improved with the implementation of other methods. Similar to the initial proposal of the framework, this paper proposes the recognition of eight ADL, e.g., walking, running, standing, going upstairs, going downstairs, driving, sleeping, and watching television, and nine environments, e.g., bar, hall, kitchen, library, street, bedroom, living room, gym, and classroom, but using the Instance Based k-nearest neighbour (IBk) and AdaBoost methods as well. The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.

**Keywords:** activities of daily living; AdaBoost; mobile devices; artificial neural networks; deep neural networks
