Edge Computing Transformers for Fall Detection in Older Adults
The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and quality of life, and placing an additional burden on healthcare systems. Existing fa...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:dnet:ruidera_____::a48af3481101465338d56129d71f69d2 |
| Acceso en línea: | https://doi.org/10.1142/S0129065724500266 https://hdl.handle.net/10578/48282 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep Learning Fall detection Inertial Measurement Unit Older adults Transformer Neural Network |
| Sumario: | The global trend of increasing life expectancy introduces new challenges with far-reaching implications. Among these, the risk of falls among older adults is particularly significant, affecting individual health and quality of life, and placing an additional burden on healthcare systems. Existing fall detection systems often have limitations, including delays due to continuous server communication, high false-positive rates, low adoption rates due to wearability and comfort issues, and high costs. In response to these challenges, this work presents a reliable, wearable, and cost-effective fall detection system. The proposed system consists of a fit-for-purpose device, with an embedded algorithm and an Inertial Measurement Unit (IMU), enabling real-time fall detection. The algorithm combines a Threshold Based Algorithm (TBA) and a neural network with low number of parameters based on a transformer architecture. This system demonstrates notable performance with 95.29% accuracy, 93.68% specificity and 96.66% sensitivity, while only using a 0.38% of the trainable parameters used by the next approach. |
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