A survey on deep learning architectures in human activities recognition application in sports science, healthcare, and security
Conference paper
Adel, Basant, Badran, Asmaa, Elshami, Nada, Salah, Ahmad, Fathalla, Ahmed and Bekhit, Mahmoud. (2022). A survey on deep learning architectures in human activities recognition application in sports science, healthcare, and security. ICR 2022 International Conference on Innovations in Computing Research. Athens, Greece 29 - 31 Aug 2022 Switzerland: Springer Nature. pp. 121 - 134 https://doi.org/10.1007/978-3-031-14054-9_13
Authors | Adel, Basant, Badran, Asmaa, Elshami, Nada, Salah, Ahmad, Fathalla, Ahmed and Bekhit, Mahmoud |
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Type | Conference paper |
Abstract | In a typical human activity recognition (HAR) system, human activities are recognized by collecting data from inertial sensors (i.e., Inertial measurement unit (IMU)) or visual sensors (i.e., cameras). Then, the collected data is labelled with human activities. In turn, the data is used to train machine learning (ML) or deep learning (DL) models. HAR systems are widely used in different applications such as security, healthcare, the Internet of Things (IoT), and sports domains. The highest accuracy rates are achieved by DL models. In this context, we review the recent advancements of HAR systems in three trendy domains, namely, 1) sports science, 2) healthcare, and 3) security. The aim of this review is to reveal the most widely used DL architectures alongside the highest achieved accuracy rates in each of these domains. Both the Convolution Neural Network (CNN) and the Long Short Term Memory (LSTM) architectures achieved the best performance in both fields of sports science and healthcare. In the security field, the best performance was achieved by the adapted VGG-16 model. |
Keywords | Deep learning; HAR; Healthcare ; Security; Sports science |
Year | 01 Jan 2022 |
Publisher | Springer Nature |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-14054-9_13 |
Web address (URL) | https://link.springer.com/chapter/10.1007/978-3-031-14054-9_13 |
Open access | Published as non-open access |
Research or scholarly | Research |
Publisher's version | License All rights reserved File Access Level Controlled |
Page range | 121 - 134 |
Web address (URL) of conference proceedings | https://link.springer.com/book/10.1007/978-3-031-14054-9 |
Output status | Published |
Publication dates | |
2022 | |
Publication process dates | |
Accepted | 2022 |
Deposited | 05 Aug 2024 |
Additional information | © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. |
Place of publication | Switzerland |
https://acuresearchbank.acu.edu.au/item/90q07/a-survey-on-deep-learning-architectures-in-human-activities-recognition-application-in-sports-science-healthcare-and-security
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