Deep Learning Human Activity Recognition
| dc.contributor.author | Browne, David | |
| dc.contributor.author | Giering, Michael | |
| dc.contributor.author | Prestwich, Steven D. | |
| dc.contributor.editor | Curry, Edward | |
| dc.contributor.editor | Keane, Mark | |
| dc.contributor.editor | Ojo, Adegboyega | |
| dc.contributor.editor | Salwala, Dhaval | |
| dc.contributor.funder | Science Foundation Ireland | en |
| dc.contributor.funder | United Technologies Research Center | en |
| dc.date.accessioned | 2020-05-18T11:58:39Z | |
| dc.date.available | 2020-05-18T11:58:39Z | |
| dc.date.issued | 2019-12 | |
| dc.date.updated | 2020-05-18T11:34:21Z | |
| dc.description.abstract | Human activity recognition is an area of interest in various domains such as elderly and health care, smart-buildings and surveillance, with multiple approaches to solving the problem accurately and efficiently. For many years hand-crafted features were manually extracted from raw data signals, and activities were classified using support vector machines and hidden Markov models. To further improve on this method and to extract relevant features in an automated fashion, deep learning methods have been used. The most common of these methods are Long Short-Term Memory models (LSTM), which can take the sequential nature of the data into consideration and outperform existing techniques, but which have two main pitfalls; longer training times and loss of distant pass memory. A relevantly new type of network, the Temporal Convolutional Network (TCN), overcomes these pitfalls, as it takes significantly less time to train than LSTMs and also has a greater ability to capture more of the long term dependencies than LSTMs. When paired with a Convolutional Auto-Encoder (CAE) to remove noise and reduce the complexity of the problem, our results show that both models perform equally well, achieving state-of-the-art results, but when tested for robustness on temporal data the TCN outperforms the LSTM. The results also show, for industry applications, the TCN can accurately be used for fall detection or similar events within a smart building environment. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Published Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Browne, D., Giering, M. and Prestwich, S. (2019) 'Deep Learning Human Activity Recognition', Proceedings of the 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2019), NUI Galway, Ireland, 5-6 December. CEUR Workshop Proceedings, Vol. 2563, pp. 76-87. Available at: http://ceur-ws.org/Vol-2563/aics_9.pdf (Accessed: 18 May 2020) | en |
| dc.identifier.endpage | 87 | en |
| dc.identifier.issn | 1613-0073 | |
| dc.identifier.journaltitle | CEUR Workshop Proceedings | en |
| dc.identifier.startpage | 76 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/9981 | |
| dc.identifier.volume | 2563 | en |
| dc.language.iso | en | en |
| dc.publisher | Sun SITE Central Europe | en |
| dc.relation.project | info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ | en |
| dc.relation.uri | http://ceur-ws.org/Vol-2563/aics_9.pdf | |
| dc.relation.uri | http://ceur-ws.org/Vol-2563/ | |
| dc.rights | © 2019, the Authors. This paper is published under the Creative Commons License Attribution 4.0 International (CC BY 4.0). | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Temporal Convolutional Network | en |
| dc.subject | TCN | en |
| dc.subject | Convolutional Auto-Encoder | en |
| dc.subject | CAE | en |
| dc.subject | Fall detection | en |
| dc.subject | Smart building | en |
| dc.subject | Long Short-Term Memory | en |
| dc.subject | LSTM | en |
| dc.title | Deep Learning Human Activity Recognition | en |
| dc.type | Conference item | en |
