@article{ref1,
title="Estimating normal and abnormal activities using smartphones",
journal="Studies in health technology and informatics",
year="2016",
author="Chatzaki, Charikleia and Pediaditis, Matthew and Vavoulas, George and Tsiknakis, Manolis",
volume="224",
number="",
pages="195-200",
abstract="The main objective of this study is to propose a computational pipeline for the recognition of normal and abnormal activities based on smartphone accelerometer data. <br><br>METHODS and techniques that have been previously evaluated are further evolved and applied for the recognition of a large set of separate activities as well as a sequence of activities simulating a common scenario of daily living as a more realistic approach. For these purposes, the MobiAct dataset which encompass a set of normal activities of daily living (ADLs) and abnormal activities (falls) was used. The results show a classification accuracy of 99% for the recognition of separate ADLs, while a reduction of 5% is observed for the recognition of the scenarios.<p /> <p>Language: en</p>",
language="en",
issn="0926-9630",
doi="",
url="http://dx.doi.org/"
}