An effective video-based model for fall monitoring of the elderly
| Autor: | Jenq-Neng Hwang, Pham Van Tuan, Hoang Le Uyen Thuc |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Background subtraction
Engineering Short Message Service Event (computing) business.industry 010401 analytical chemistry Feature extraction 02 engineering and technology Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences Gait (human) Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Segmentation Artificial intelligence Hidden Markov model business computer |
| Zdroj: | 2017 International Conference on System Science and Engineering (ICSSE). |
| DOI: | 10.1109/icsse.2017.8030835 |
| Popis: | Fall is one of the major health challenges facing the elderly adults, especially the adults with high fall risk factors. In this paper, we aim to build a video-based model to mitigate the consequences of fall of the elderly at two application scenarios: (1) predict the fall risk caused by unbalanced gait and (2) detect a fall event as soon as it happens. In the first stage, we use a common camera to capture the video of a person moving such as doing actions or walking. In the second stage, our proposed model for both scenarios follows the same three-module structure: (1) human object segmentation using background subtraction, (2) feature representation using two separate feature descriptors, one for each application scenario, and (3) abnormal event detection based on Hidden Markov Model. The final stage is to convey an SMS message to the pre-defined cell phone number to notify the caregiver of detected anomaly. Experimental results show the promising performance of the proposed model in terms of the acceptable accuracy and the low processing time. |
| Databáze: | OpenAIRE |
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