Pires, I.M.; Garcia, N.M.; Pombo, N.; Flórez-Revuelta, F.; Spinsante, S. Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices. Sensors2018, 18, 640.
Pires, I.M.; Garcia, N.M.; Pombo, N.; Flórez-Revuelta, F.; Spinsante, S. Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices. Sensors 2018, 18, 640.
Pires, I.M.; Garcia, N.M.; Pombo, N.; Flórez-Revuelta, F.; Spinsante, S. Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices. Sensors2018, 18, 640.
Pires, I.M.; Garcia, N.M.; Pombo, N.; Flórez-Revuelta, F.; Spinsante, S. Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices. Sensors 2018, 18, 640.
Abstract
Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.
Keywords
Activities of Daily Living (ADL); environment; sensors; mobile devices, framework; data acquisition; data processing; data fusion; pattern recognition; machine learning
Subject
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
