Machine learning‐based edge‐computing on a multi‐level architecture of WSN and IoT for real‐time fall detection-Reference-Cited by-同舟云学术

Machine learning‐based edge‐computing on a multi‐level architecture of WSN and IoT for real‐time fall detection

Published:2020-10-21 Issue:6 Volume:10 Page:320-332
ISSN:2043-6386
Container-title:IET Wireless Sensor Systems
language:en
Short-container-title:IET wirel. sens. syst.

Author:

El Attaoui Amina¹^ORCID,Largo Salma²,Kaissari Soufiane¹,Benba Achraf¹,Jilbab Abdelilah¹,Bourouhou Abdennaser¹

Affiliation:

1. Electronics Sensors, Systems, and Nanobiotechnologies, STIS, ENSET, Mohammed V University in RabatMorocco

2. Smart, Embedded, Enterprise, and Distributed Systems (SEEDS), STRS Laboratory. INPT of RabatMorocco

Publisher

Institution of Engineering and Technology (IET)

Subject

Industrial and Manufacturing Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-wss.2020.0091

Reference46 articles.

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3. SchottN. KurzA.‐K.: ‘Stürze bei älteren Erwachsenen: Risikofaktoren – Assessment – Prävention | Zeitschrift für Sportpsychologie | Vol 15 No 2’. Accessed 9 June 2020. Available athttps://econtent.hogrefe.com/doi/abs/10.1026/1612‐5010.15.2.45

4. Determining Risk of Falls in Community Dwelling Older Adults: A Systematic Review and Meta-analysis Using Posttest Probability

5. Automatic Fall Monitoring: A Review

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