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http://hdl.handle.net/10609/151749
Title: | Diseño de un sistema antiahogamiento, basado en LoRa, integrado en una Smart Beach |
Author: | Sospedra Iglesias, Joaquim |
Tutor: | Saura Mas, Xavier |
Others: | Monzo, Carlos ![]() |
Abstract: | Both globally and locally, drowning deaths in the aquatic environment (rivers, beaches, swimming pools, etc.) resulting from recreational activities, mainly bathing, are a constant year after year. The complexity of the environment, especially in open spaces, makes it difficult to develop and implement systems to reduce the accident rate, even in those that already have a certain surveillance infrastructure, as is the case of a Smart-Beach. In this context, the purpose of this Master's Thesis is to develop a system for personal use to minimise the risk of drowning on a beach. This system is conceived in such a way that it can be integrated into the environment of a Smart-Beach, taking advantage of and supporting itself on the existing infrastructures of the same, thus increasing its usefulness by providing them with a new functionality. The system designed allows the user to receive notifications about situations of interest in the beach environment, basically about potentially dangerous situations, as well as acting as an autonomous surveillance and prevention system, which is activated when a potentially dangerous situation is detected that could lead to a possible drowning. After the conceptual and detailed design of the system, based on a review of the state of the art in the field, an assessment of implementation costs is also carried out, as well as the identification of future work. |
Keywords: | anti-ahogamiento Smart Beach airbag LoRa smartwatch signos vitales sensores BLE |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 8-Jan-2024 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ ![]() |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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qsospeiTFG0125memoria.pdf | Memoria del TFM | 24,24 MB | Adobe PDF | ![]() View/Open |
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