دورية أكاديمية
A study of deep neural networks for human activity recognition
| العنوان: | A study of deep neural networks for human activity recognition |
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| المؤلفون: | Sansano-Sansano, Emilio, Montoliu Colás, Raul, Belmonte-Fernández, Óscar |
| بيانات النشر: | Wiley |
| سنة النشر: | 2020 |
| المجموعة: | Repositori Universitat Jaume I (Repositorio UJI) |
| مصطلحات موضوعية: | convolutional neural network, deep learning, human activity recognition, recurrent neural network |
| الوصف: | Human activity recognition and deep learning are two fields that have attracted attention in recent years. The former due to its relevance in many application domains, such as ambient assisted living or health monitoring, and the latter for its recent and excellent performance achievements in different domains of application such as image and speech recognition. In this article, an extensive analysis among the most suited deep learning architectures for activity recognition is conducted to compare its performance in terms of accuracy, speed, and memory requirements. In particular, convolutional neural networks (CNN), long short‐term memory networks (LSTM), bidirectional LSTM (biLSTM), gated recurrent unit networks (GRU), and deep belief networks (DBN) have been tested on a total of 10 publicly available datasets, with different sensors, sets of activities, and sampling rates. All tests have been designed under a multimodal approach to take advantage of synchronized raw sensor' signals. Results show that CNNs are efficient at capturing local temporal dependencies of activity signals, as well as at identifying correlations among sensors. Their performance in activity classification is comparable with, and in most cases better than, the performance of recurrent models. Their faster response and lower memory footprint make them the architecture of choice for wearable and IoT devices. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 0824-7935 1467-8640 |
| العلاقة: | Computational Intelligence, 2020; Spanish Ministry of Science, Innovation and Universities through the “Proyectos I + D Retos investigación” programme: RTI2018‐095168‐B‐C53; Jaume I University “Research promotion plan 2017” programme: UJI‐B2017‐45; Sansano, E, Montoliu, R, Belmonte Fernández, Ó. A study of deep neural networks for human activity recognition. Computational Intelligence. 2020; 1– 27. https://doi.org/10.1111/coin.12318Test; http://hdl.handle.net/10234/187531Test; https://doi.org/10.1111/coin.12318Test |
| DOI: | 10.1111/coin.12318 |
| الإتاحة: | https://doi.org/10.1111/coin.12318Test http://hdl.handle.net/10234/187531Test |
| حقوق: | Copyright © John Wiley & Sons, Inc. ; http://rightsstatements.org/vocab/InC/1.0Test/ ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.FFB5C069 |
| قاعدة البيانات: | BASE |
| تدمد: | 08247935 14678640 |
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| DOI: | 10.1111/coin.12318 |