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dc.contributor.author | Sarabia-Jácome, David | es_ES |
dc.contributor.author | Usach, Regel | es_ES |
dc.contributor.author | Palau Salvador, Carlos Enrique | es_ES |
dc.contributor.author | Esteve Domingo, Manuel | es_ES |
dc.date.accessioned | 2021-07-20T03:30:39Z | |
dc.date.available | 2021-07-20T03:30:39Z | |
dc.date.issued | 2020-09 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/169535 | |
dc.description.abstract | [EN] Falls is one of most concerning accidents in aged population due to its high frequency and serious repercussion; thus, quick assistance is critical to avoid serious health consequences. There are several Ambient Assisted Living (AAL) solutions that rely on the technologies of the Internet of Things (IoT), Cloud Computing and Machine Learning (ML). Recently, Deep Learning (DL) have been included for its high potential to improve accuracy on fall detection. Also, the use of fog devices for the ML inference (detecting falls) spares cloud drawback of high network latency, non-appropriate for delay-sensitive applications such as fall detectors. Though, current fall detection systems lack DL inference on the fog, and there is no evidence of it in real environments, nor documentation regarding the complex challenge of the deployment. Since DL requires considerable resources and fog nodes are resource-limited, a very efficient deployment and resource usage is critical. We present an innovative highly-efficient intelligent system based on a fog-cloud computing architecture to timely detect falls using DL technics deployed on resource-constrained devices (fog nodes). We employ a wearable tri-axial accelerometer to collect patient monitoring data. In the fog, we propose a smart-IoT-Gateway architecture to support the remote deployment and management of DL models. We deploy two DL models (LSTM/GRU) employing virtualization to optimize resources and evaluate their performance and inference time. The results prove the effectiveness of our fall system, that provides a more timely and accurate response than traditional fall detector systems, higher efficiency, 98.75% accuracy, lower delay, and service improvement. | es_ES |
dc.description.sponsorship | This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's Horizon 2020 research and innovation program as part of the ACTIVAGE project under Grant 732679. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Internet of Things | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | IoT | es_ES |
dc.subject | Big data | es_ES |
dc.subject | Fog computing | es_ES |
dc.subject | Cloud computing | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | AAL | es_ES |
dc.subject | Health | es_ES |
dc.subject | AHA, Fall | es_ES |
dc.subject.classification | INGENIERIA TELEMATICA | es_ES |
dc.title | Highly-efficient fog-based deep learning AAL fall detection system | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.iot.2020.100185 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions | es_ES |
dc.description.bibliographicCitation | Sarabia-Jácome, D.; Usach, R.; Palau Salvador, CE.; Esteve Domingo, M. (2020). Highly-efficient fog-based deep learning AAL fall detection system. Internet of Things. 11:1-19. https://doi.org/10.1016/j.iot.2020.100185 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.iot.2020.100185 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 19 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 11 | es_ES |
dc.identifier.eissn | 2542-6605 | es_ES |
dc.relation.pasarela | S\436982 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, Ecuador | es_ES |
dc.description.references | “World Population Ageing.” [Online]. Available: http://www.un.org/esa/population/publications/worldageing19502050/. [Accessed: 23-Sep-2018]. | es_ES |
dc.description.references | “Falls, ” World Health Organization. [Online]. Available: http://www.who.int/news-room/fact-sheets/detail/falls. [Accessed: 20-Sep-2018]. | es_ES |
dc.description.references | Rashidi, P., & Mihailidis, A. (2013). A Survey on Ambient-Assisted Living Tools for Older Adults. IEEE Journal of Biomedical and Health Informatics, 17(3), 579-590. doi:10.1109/jbhi.2012.2234129 | es_ES |
dc.description.references | Bousquet, J., Kuh, D., Bewick, M., Strandberg, T., Farrell, J., Pengelly, R., … Bringer, J. (2015). Operative definition of active and healthy ageing (AHA): Meeting report. Montpellier October 20–21, 2014. European Geriatric Medicine, 6(2), 196-200. doi:10.1016/j.eurger.2014.12.006 | es_ES |
dc.description.references | “WHO | What is Healthy Ageing?”[Online]. Available: http://www.who.int/ageing/healthy-ageing/en/. [Accessed: 19-Sep-2018]. | es_ES |
dc.description.references | Fei, X., Shah, N., Verba, N., Chao, K.-M., Sanchez-Anguix, V., Lewandowski, J., … Usman, Z. (2019). CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey. Future Generation Computer Systems, 90, 435-450. doi:10.1016/j.future.2018.06.042 | es_ES |
dc.description.references | W. Zaremba, “Recurrent neural network regularization,” no. 2013, pp. 1–8, 2015. | es_ES |
dc.description.references | Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735 | es_ES |
dc.description.references | J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” pp. 1–9, 2014. | es_ES |
dc.description.references | N. Zerrouki, F. Harrou, Y. Sun, and A. Houacine, “Vision-based human action classification,” vol. 18, no. 12, pp. 5115–5121, 2018. | es_ES |
dc.description.references | Panahi, L., & Ghods, V. (2018). Human fall detection using machine vision techniques on RGB–D images. Biomedical Signal Processing and Control, 44, 146-153. doi:10.1016/j.bspc.2018.04.014 | es_ES |
dc.description.references | Y. Li, K.C. Ho, and M. Popescu, “A microphone array system for automatic fall detection,” vol. 59, no. 2, pp. 1291–1301, 2012. | es_ES |
dc.description.references | Taramasco, C., Rodenas, T., Martinez, F., Fuentes, P., Munoz, R., Olivares, R., … Demongeot, J. (2018). A Novel Monitoring System for Fall Detection in Older People. IEEE Access, 6, 43563-43574. doi:10.1109/access.2018.2861331 | es_ES |
dc.description.references | C. Wang et al., “Low-power fall detector using triaxial accelerometry and barometric pressure sensing,” vol. 12, no. 6, pp. 2302–2311, 2016. | es_ES |
dc.description.references | S.B. Khojasteh and E. De Cal, “Improving fall detection using an on-wrist wearable accelerometer,” pp. 1–28. | es_ES |
dc.description.references | Theodoridis, T., Solachidis, V., Vretos, N., & Daras, P. (2017). Human Fall Detection from Acceleration Measurements Using a Recurrent Neural Network. IFMBE Proceedings, 145-149. doi:10.1007/978-981-10-7419-6_25 | es_ES |
dc.description.references | F. Sposaro and G. Tyson, “iFall : an android application for fall monitoring and response,” pp. 6119–6122, 2009. | es_ES |
dc.description.references | A. Ngu, Y. Wu, H. Zare, A.P. B, B. Yarbrough, and L. Yao, “Fall detection using smartwatch sensor data with accessor architecture,” vol. 2, pp. 81–93. | es_ES |
dc.description.references | P. Jantaraprim and P. Phukpattaranont, “Fall detection for the elderly using a support vector machine,” no. 1, pp. 484–490, 2012. | es_ES |
dc.description.references | Aziz, O., Musngi, M., Park, E. J., Mori, G., & Robinovitch, S. N. (2016). A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Medical & Biological Engineering & Computing, 55(1), 45-55. doi:10.1007/s11517-016-1504-y | es_ES |
dc.description.references | V. Carletti, A. Greco, A. Saggese, and M. Vento, “A smartphone-based system for detecting falls using anomaly detection,” vol. 6978, 2017, pp. 490–499. | es_ES |
dc.description.references | Yacchirema, D., de Puga, J. S., Palau, C., & Esteve, M. (2018). Fall detection system for elderly people using IoT and Big Data. Procedia Computer Science, 130, 603-610. doi:10.1016/j.procs.2018.04.110 | es_ES |