HIGHLIGHTS
- who: SARR JURAEV et al. from the Department of Information and Communication Engineering, Inha University, Incheon, South Korea have published the article: Overleaf Example, in the Journal: (JOURNAL)
- what: Third this study shows that a Transformer network applied to elderly action recognition outperforms LSTM-based networks by a noticeable margin. The lack of real-world elderly activity datasets has limited current fall detection research focusing mainly on scientific datasets, , , , and_[10]. Perhaps most importantly, this study examined the benefits from using synthetic data for pose-based action recognition and fall detection due to the limited . . .
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