Academic Journal
Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices
| العنوان: | Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices |
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| المؤلفون: | Abdessamad Youssfi Alaoui, Youness Tabii, Rachid Oulad Haj Thami, Mohamed Daoudi, Stefano Berretti, Pietro Pala |
| المصدر: | Journal of Imaging, Vol 7, Iss 7, p 109 (2021) |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | fall detection, healthcare, positive semidefinite matrices, Riemann manifold, Dynamic Time Warping, Gram matrix, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Falls are one of the most critical health care risks for elderly people, being, in some adverse circumstances, an indirect cause of death. Furthermore, demographic forecasts for the future show a growing elderly population worldwide. In this context, models for automatic fall detection and prediction are of paramount relevance, especially AI applications that use ambient, sensors or computer vision. In this paper, we present an approach for fall detection using computer vision techniques. Video sequences of a person in a closed environment are used as inputs to our algorithm. In our approach, we first apply the V2V-PoseNet model to detect 2D body skeleton in every frame. Specifically, our approach involves four steps: (1) the body skeleton is detected by V2V-PoseNet in each frame; (2) joints of skeleton are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank 2 to build time-parameterized trajectories; (3) a temporal warping is performed on the trajectories, providing a (dis-)similarity measure between them; (4) finally, a pairwise proximity function SVM is used to classify them into fall or non-fall, incorporating the (dis-)similarity measure into the kernel function. We evaluated our approach on two publicly available datasets URFD and Charfi. The results of the proposed approach are competitive with respect to state-of-the-art methods, while only involving 2D body skeletons. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2313-433X |
| Relation: | https://www.mdpi.com/2313-433X/7/7/109; https://doaj.org/toc/2313-433X |
| DOI: | 10.3390/jimaging7070109 |
| URL الوصول: | https://doaj.org/article/336cf31b74e04fd7934f8695c14e93b6 |
| رقم الانضمام: | edsdoj.336cf31b74e04fd7934f8695c14e93b6 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2313433X |
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| DOI: | 10.3390/jimaging7070109 |