Human Activity Recognition with Convolutional Neural Networks

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Title: Human Activity Recognition with Convolutional Neural Networks
Authors: Bevilacqua, Antonio
MacDonald, Kyle
Rangarej, Aamina
Widjaya, Venessa
Caulfield, Brian
Kechadi, Tahar
Permanent link: http://hdl.handle.net/10197/10546
Date: 14-Sep-2018
Online since: 2019-05-20T13:42:12Z
Abstract: The problem of automatic identification of physical activities performed by human subjects is referred to as Human Activity Recognition (HAR). There exist several techniques to measure motion characteristics during these physical activities, such as Inertial Measurement Units (IMUs). IMUs have a cornerstone position in this context, and are characterized by usage flexibility, low cost, and reduced privacy impact. With the use of inertial sensors, it is possible to sample some measures such as acceleration and angular velocity of a body, and use them to learn models that are capable of correctly classifying activities to their corresponding classes. In this paper, we propose to use Convolutional Neural Networks (CNNs) to classify human activities. Our models use raw data obtained from a set of inertial sensors. We explore several combinations of activities and sensors, showing how motion signals can be adapted to be fed into CNNs by using different network architectures. We also compare the performance of different groups of sensors, investigating the classification potential of single, double and triple sensor systems. The experimental results obtained on a dataset of 16 lower-limb activities, collected from a group of participants with the use of five different sensors, are very promising.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Series/Report no.: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III; Lecture Notes in Computer Science book series (LNCS, volume 11053); Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11053)
Copyright (published version): 2018 Springer Nature Switzerland AG
Keywords: Human activity recognitionConvolutional neural networks (CNN)Deep learningClassificationInertial Measurement Units (IMU)
DOI: 10.1007/978-3-030-10997-4_33
Other versions: http://www.ecmlpkdd2018.org/
Language: en
Status of Item: Peer reviewed
Is part of: Brefeld, U., Curry, E., Daly, E., MacNamee, B., Marascu, A., Pinelli, F., Berlingerio, M., Hurley, N. (eds.). Machine Learning and Knowledge Discovery in Databases
Conference Details: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Dublin, Ireland. 10-14 September 2018
Appears in Collections:Public Health, Physiotherapy and Sports Science Research Collection
Insight Research Collection

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