Authors : Areege Samir Elhosany; Salma Ahmed Khalil; Mohamed Saied El-Sayed Amer

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/4yd75ckz

Scribd : https://tinyurl.com/4abu9mbw

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN071

Abstract : Nowadays, falls put patients' safety and health in danger in hospitals and assisted living facilities, especially at night. The camera system used in this study is the suggested model; it records video footage and, in real time, analyzes patterns using Media Pipe algorithms to detect fainting or falls. An instant alert is generated and sent to a mobile app connected to the camera system if such an event is detected. The caretakers, nurses, or security officers entrusted with providing assistance can communicate with each other using the smartphone application. They follow the message to a specific location. Lastly, the system aims to improve the quality of care and support for the senior population by accelerating the response to instances of fainting and falling through the use of computer vision technologies and real-time notifications.

Keywords : Falling People, Human Detection, Media Pipe Fainting People, Elder Care.

References :

[1].    So, I., Han, D., Kang, S., Kim, Y., & Jung, S. (2008). Recognition of Fainting Motion from Fish-eye Lens Camera Images.
[2].    Sarafianos, S.; Boteanu, B.; Ionescu, B.; Kakadiaris, I.A. 3D human pose estimation: A review of the literature and analysis of covariates. Comput. Vis. Image Underst. 2016,152, 1–20. [CrossRef]
[3].    Chen, Y.; Tian, Y.; He, M. Monocular human pose estimation: A survey of deep learning-based methods. Comput. Vis. Image Underst. 2020,192, 102897. [CrossRef]
[4].    Wang, J.; Tan, S.; Zhen, X.; Xu, S.; Zheng, F.; He, Z.; Shao, L. Deep 3D human pose estimation: A review. Comput. Vis. Image Underst. 2021,210, 103225. [CrossRef]
[5].    Yurtsever, M.M.E.; Eken, S. BabyPose: Real-time decoding of baby’s non-verbal communication using 2D video-based pose estimation. IEEE Sens. 2022,22, 13776–13784. [CrossRef]
[6].    Alam, E.; Sufian, A.; Dutta, P.; Leo, M. Vision-based human fall detection systems using deep learning: A review. Comput. Biol. Med. 2022,146, 105626. [CrossRef]
[7].    Kim, Jong-Wook & Choi, Jin-Young & Ha, Eun-Ju & Choi, Jae-Ho. (2023). Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model. Applied Sciences. 13. 2700. 10.3390/app13042700.
[8].    Wang, Zhen-hai & Xu, Bo. (2015). A Robust Home Alone Faint Detection Based on Wireless Sensor Networks. International Journal of Distributed Sensor Networks. 2015. 1-5. 10.1155/2015/534980.
[9].    W. K. Wong, H. L. Lim, C. K. Loo and W. S. Lim, "Home Alone Faint Detection Surveillance System Using Thermal Camera," 2010 Second International Conference on Computer Research and Development, Kuala Lumpur, Malaysia, 2010, pp. 747-751, doi: 10.1109/ICCRD.2010.163.
[10].    Nguyen, T.B., Nguyen, V.T., Chung, S., & Cho, S. (2016). Real-time Human Detection under Omni-dir ectional Camera based on CNN with Unified Detection and AGMM for Visual Surveillance. Journal of Korea Multimedia Society, 19, 1345-1360.
[11].    Bazarevsky, V.; Grishchenko, I. On-Device, Real-Time Body Pose Tracking with MediaPipe BlazePose, Google Research. Available online: https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html (accessed on 10 August 2021).
[12].    MediaPipe Pose. Available online: https://google.github.io/mediapipe/solutions/pose.html (accessed on 28 December 2021).