<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Li, Frédéric</style></author><author><style face="normal" font="default" size="100%">Shirahama, Kimiaki</style></author><author><style face="normal" font="default" size="100%">Nisar, Muhammad Adeel</style></author><author><style face="normal" font="default" size="100%">Köping, Lukas</style></author><author><style face="normal" font="default" size="100%">Grzegorzek, Marcin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.</style></title><secondary-title><style face="normal" font="default" size="100%">Sensors (Basel, Switzerland)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sensors (Basel)</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Human Activities</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks (Computer)</style></keyword><keyword><style  face="normal" font="default" size="100%">Wearable Electronic Devices</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018 Feb 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">18</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/29495310?dopt=Abstract</style></custom1></record></records></xml>