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商品詳細
Challenges and Trends in Multimodal Fall Detection for Healthcare. 2020 ed.
・ISBN 978-3-030-38750-1 paper EUR 99.99
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お気に入り
★★★
| 著者・編者 | Ponce, Hiram / Martinez-Villasenor, Lourdes / Brieva, Jorge / Moya-Albor, Ernesto (eds.), |
|---|---|
| シリーズ | (Studies in Systems, Decision and Control) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2021 |
| ページ数 | 259 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-82926＞ |
解説
This book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion.
It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples.
This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human-machine interaction, among others.
It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state of the art in human fall and activity recognition systems, while the second part describes a public dataset especially curated for multimodal fall detection. It also gathers contributions demonstrating the use of this dataset and showing examples.
This book is useful for anyone who is interested in fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human-machine interaction, among others.