Identification of activities of daily living using sensors available in off-the-shelf mobile devices: Research and hypothesis

Abstract

This paper presents a PhD project related to the identification of a set of Activities of Daily Living (ADLs) using different techniques applied to the sensors available in off-the-shelf mobile devices. This project consists on the creation of new methodologies, to identify ADLs, and to present some concepts, such as definition of the set of ADLs relevant to be identified, the mobile device as a multi-sensor system, review of the best techniques for data acquisition, data processing, data validation, data imputation, and data fusion processes, and creation of the methods for the identification of ADLs with data mining, pattern recognition and/or machine learning techniques. However, mobile devices present several limitations, therefore techniques at each stage have to be adapted. As result of this study, we presented a brief review of the state-of-the-art related to the several parts of a mobile-system for the identification of the ADLs. Currently, the main focus consists on the study for the creation of a new method based on the analysis of audio fingerprinting samples in some Ambient Assisted Living (AAL) scenarios.