According to the World Health Organization (WHO), falls are the second cause of death due to accidental injuries, and older adults are the ones who suffer the most from them. In Ecuador, there are about 1,300,000 older adults, and falls are a major problem for their quality of life. For this reason, in this article, we present a low-cost prototype system for the monitoring and detection of falls, with the aim of providing support for the care of older adults. This tool is based on a module that applies computer vision and image processing techniques, convolutional neural networks (CNNs) and Web and mobile applications. They allow the monitoring and control of falls. To test the operation of the system, tests were carried out with fifteen volunteers. It was determined that the system managed to correctly detect 80% of fall-related events.
|Title of host publication||Communication and Applied Technologies - Proceedings of ICOMTA 2022|
|Editors||Paulo Carlos López-López, Ángel Torres-Toukoumidis, Andrea De-Santis, Óscar Avilés, Daniel Barredo|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||10|
|State||Published - 2023|
|Event||International Conference on Communication and Applied Technologies, ICOMTA 2022 - Cuenca, Ecuador|
Duration: 7 Sep 2022 → 9 Sep 2022
|Name||Smart Innovation, Systems and Technologies|
|Conference||International Conference on Communication and Applied Technologies, ICOMTA 2022|
|Period||7/09/22 → 9/09/22|
Bibliographical noteFunding Information:
Acknowledgements This work has been funded by the “Sistemas Inteligentes de Soporte a la Educación (v5)” research project, the Cátedra UNESCO “Tecnologías de apoyo para la Inclusión Educativa” initiative, and the Research Group on Artificial Intelligence and Assistive Technologies (GI-IATa) of the Universidad Politécnica Salesiana, Campus Cuenca.
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Convolutional neural networks
- Elderly people
- Fall detection Computer vision