Detalhes bibliográficos
| Resumo: | 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. |
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| Autores principais: | Kalbermatter, Rebeca B. |
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| Outros Autores: | Franco, Tiago; Pereira, Ana I.; Valente, António; Soares, Salviano Pinto; Lima, José |
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| Assunto: | Fall detection Pose model Ambient assisted-living |
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| Ano: | 2024 |
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| País: | Portugal |
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| Tipo de documento: | comunicação em conferência |
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| Tipo de acesso: | acesso restrito |
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| Instituição associada: | Instituto Politécnico de Bragança |
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| Idioma: | inglês |
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| Origem: | Biblioteca Digital do IPB |
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