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Publicação
Automatic Fall Detection with Thermal Camera
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| 3.09 MB | Adobe PDF |
Orientador(es)
Resumo(s)
People are living longer, promoting new challenges in healthcare.
Many older adults prefer to age in their own homes rather than in
healthcare institutions. Portugal has seen a similar trend, and public and
private home care solutions have been developed. However, age-related
pathologies can affect an elderly person’s ability to perform daily tasks
independently. Ambient Assisted Living (AAL) is a domain that uses
information and communication technologies to improve the quality of
life of older adults. AI-based fall detection systems have been integrated
into AAL studies, and posture estimation tools are important for monitoring
patients. In this study, the OpenCV and the YOLOv7 machine
learning framework are used to develop a fall detection system based on
posture analysis. To protect patient privacy, the use of a thermal camera
is proposed to prevent facial recognition. The developed system was
applied and validated in the real scenario.
Descrição
Palavras-chave
Fall detection Pose model Ambient assisted-living
Contexto Educativo
Citação
Kalbermatter, Rebeca B.; Franco, Tiago; Pereira, Ana I.; Valente, António; Soares, Salviano Pinto; Lima, José (2024). Automatic Fall Detection with Thermal Camera. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 1, p. 347–359. ISBN 978-3-031-53024-1
Editora
Springer Nature