A review on the artificial intelligence algorithms for the recognition of Activities of Daily Living using sensors in mobile devices

Abstract

The automatic recognition of Activities of Daily Living (ADL) with a multi-sensor mobile device that can acquire different types of sensors' data, and rely on the use of machine learning methods to handle the recognition of ADL with reliable accuracy. This paper focuses on the literature review of the existing methods to make the identification of ADL in order to assess the efficiency of the different methods for the identification of ADL and their environments using off-the-shelf mobile devices. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment. Therefore, the main purpose of this study is to present a review of the machine learning methods already used on this field, relating them with the accuracy and number of ADL recognized.