A Fallen Person Detector with a Privacy-Preserving Edge-AI Camera

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Título: A Fallen Person Detector with a Privacy-Preserving Edge-AI Camera
Autor/es: Hashemifard, Kooshan | Flórez-Revuelta, Francisco | Lacey, Gerard
Grupo/s de investigación o GITE: Informática Industrial y Redes de Computadores
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Palabras clave: Ambient-Assisted Living (AAL) | Privacy-Preserving Camera | Fallen Person Detection | Edge-AI
Fecha de publicación: 2023
Editor: SciTePress
Cita bibliográfica: Hashemifard, K.; Florez-Revuelta, F. and Lacey, G. (2023). A Fallen Person Detector with a Privacy-Preserving Edge-AI Camera. In Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE, ISBN 978-989-758-645-3; ISSN 2184-4984, SciTePress, pages 262-269. DOI: 10.5220/0012037200003476
Resumen: As the population ages, Ambient-Assisted Living (AAL) environments are increasingly used to support older individuals’ safety and autonomy. In this study, we propose a low-cost, privacy-preserving sensor system integrated with mobile robots to enhance fall detection in AAL environments. We utilized the Luxonis OAKD Edge-AI camera mounted on a mobile robot to detect fallen individuals. The system was trained using YOLOv6 network on the E-FPDS dataset and optimized with a knowledge distillation approach onto the more compact YOLOv5 network, which was deployed on the camera. We evaluated the system’s performance using a custom dataset captured with a robot-mounted camera. We achieved a precision of 96.52%, a recall of 95.10%, and a recognition rate of 15 frames per second. The proposed system enhances the safety and autonomy of older individuals by enabling the rapid detection and response to falls.
Patrocinador/es: This work has been part supported by the visuAAL project on Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living (https://www.visuaal-itn.eu/) funded by the EU H2020 Marie Skłodowska-Curie grant agreement No. 861091. The project has also been part supported by the SFI Future Innovator Award SFI/21/FIP/DO/9955 project Smart Hangar.
URI: http://hdl.handle.net/10045/134170
ISBN: 978-989-758-645-3
ISSN: 2184-4984
DOI: 10.5220/0012037200003476
Idioma: eng
Tipo: info:eu-repo/semantics/conferenceObject
Derechos: © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
Revisión científica: si
Versión del editor: https://doi.org/10.5220/0012037200003476
Aparece en las colecciones:INV - I2RC - Comunicaciones a Congresos, Conferencias, etc.
Investigaciones financiadas por la UE
INV - AmI4AHA - Comunicaciones a Congresos, Conferencias, etc.

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