Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

Sensors
Frédéric LiMarcin Grzegorzek

Abstract

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...Continue Reading

References

Oct 23, 1997·Neural Computation·S Hochreiter, J Schmidhuber
Jul 29, 2006·Science·G E Hinton, R R Salakhutdinov
May 22, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Jan C van GemertJan-Mark Geusebroek
Jun 22, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yoshua BengioPascal Vincent
Sep 21, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Mustafa Gokce BaydoganEugene Tuv
Sep 9, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Jeff DonahueTrevor Darrell

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Citations

Jan 30, 2019·Sensors·Patrícia BotaHugo Gamboa
Nov 23, 2018·Sensors·Wesllen Sousa LimaEduardo J Pereira Souto
Sep 13, 2020·International Journal of Environmental Research and Public Health·JinSoo Park, Sungroul Kim
Jun 25, 2020·Sensors·Muhammad Adeel NisarMarcin Grzegorzek
Sep 19, 2018··Wesllen Sousa LimaEduardo J. Pereira Souto

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Methods Mentioned

BETA
feature extraction

Software Mentioned

CBS
SHAR
UniMiB
Tensorflow
Activity Recognition Chain ( ARC )
Keras

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