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- W4386363426 abstract "The World Health Organization (WHO) has highlighted sarcopenia as a significant health hazard, especially in elderly people. According to the WHO, sarcopenia is the age-related loss of skeletal muscle mass, strength, and function. It may result in physical impairment, accidents, and a poor quality of life. About 10–20% of individuals over 60 have sarcopenia, and the frequency rises with age. Sarcopenia can substantially negatively influence muscle strength and quality of life, increasing the risk of disability, mortality, and falls. Wearable sensor systems might be uncomfortable for elderly people, particularly if they have sensory or mobility problems. Thus, it's crucial to create them effectively utilizing vision-based methods. In this research, we suggested a novel method for identifying older citizens’ functional mobility due to sarcopenia conditions using an attentive visual model. The system developed by automating traditional TUG tests. During the TUG test, body landmarks were utilized to predict sarcopenia by observing body posture and movement patterns. The proposed method showed promising results with 93.7% accuracy." @default.
- W4386363426 created "2023-09-02" @default.
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- W4386363426 date "2023-01-01" @default.
- W4386363426 modified "2023-09-27" @default.
- W4386363426 title "Attentive Vision-Based Model for Sarcopenia Screening by Automating Timed Up-and-Go (TUG) Test" @default.
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- W4386363426 doi "https://doi.org/10.1007/978-3-031-38281-9_4" @default.
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