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Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này:
https://elib.vku.udn.vn/handle/123456789/2748
Nhan đề: | Towards a New Multi-tasking Learning Approach for Human Fall Detection |
Tác giả: | Nguyen, Duc Anh Pham, Cuong Rob, Argent Brian, Caulfield Le, Khac Nhien An |
Từ khoá: | fall detection multi-task learning human activity recognition data scarcity Deep Learning |
Năm xuất bản: | thá-2023 |
Nhà xuất bản: | Springer Nature |
Tóm tắt: | Many fall detection systems are being used to provide real-time responses to fall occurrences. Automated fall detection is challenging because it requires near perfect accuracy to be clinically acceptable. Recent research has tried to improve the accuracy along with reducing the high rate of false positives. Nevertheless, there are still limitations in terms of having efficient learning approaches and proper datasets to train. To improve the accuracy, one approach is to include non-fall data from public datasets as negative examples to train the deep learning model. However, this approach could increase the imbalance of the training set. In this paper, we propose a multi-task deep learning model to tackle this problem. We divide datasets into multiple training sets for multiple tasks, and we prove this approach gives better results than a single-task model trained on all datasets. Many experiments are conducted to find the best combination of tasks for multi-task model training for fall detection. |
Mô tả: | Lecture Notes in Networks and Systems (LNNS, volume 734); CITA: Conference on Information Technology and its Applications; pp: 50-61. |
Định danh: | https://link.springer.com/chapter/10.1007/978-3-031-36886-8_5 http://elib.vku.udn.vn/handle/123456789/2748 |
ISBN: | 978-3-031-36886-8 |
Bộ sưu tập: | CITA 2023 (International) |
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