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• W4386025580 endingPage "92352" @default.
• W4386025580 startingPage "92322" @default.
• W4386025580 abstract "In the elderly population, falls are one of the leading causes of fatal and non-fatal injuries. Fall detection and early alarms play an important role in mitigating the negative effects of falls, especially given the growing proportion of the elderly population. Due to their non-intrusive nature, data availability, and low deployment costs, RGB videos have been used in many previous studies to detect falls. The RGB data, however, can be affected by background environment changes, resulting in non-recognition. To overcome these challenges, many researchers propose extracting skeleton data from RGB videos and using it for fall detection. Although there have been multiple surveys on fall detection, most of them focus on assessing fall detection systems using different kinds of sensors, and a comprehensive evaluation of skeleton-based fall detection in RGB videos is lacking. In this paper, we examine the most recent advances in skeleton-based fall detection in RGB videos, from handcrafted feature-based methods to advanced deep learning algorithms. Further, we present several skeleton-based fall detection techniques and their performance results on various benchmark datasets, along with challenges and future directions in this field." @default.
• W4386025580 created "2023-08-22" @default.
• W4386025580 creator A5025002122 @default.
• W4386025580 creator A5047578139 @default.
• W4386025580 creator A5048935590 @default.
• W4386025580 creator A5065725126 @default.
• W4386025580 date "2023-01-01" @default.
• W4386025580 modified "2023-09-26" @default.
• W4386025580 title "Advances in Skeleton-Based Fall Detection in RGB Videos: from Handcrafted to Deep Learning Approaches" @default.
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