Challenges and trends in multimodal fall detection for healthcare : Preface
Journal
Challenges and trends in multimodal fall detection for healthcare
Publisher
Springer
Date Issued
2020
Author(s)
Type
Resource Types::text::journal::journal article
DOI
URL
Abstract
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. ©2020 Springer Nature Switzerland AG.
Subjects
How to cite
Ponce Espinosa, H. E., Martínez Villaseñor, M. de L. G., Moya Albor, E. y Brieva Rico, J. E. (2020). Preface. En: Ponce Espinosa, H. E., Martínez Villaseñor, M. de L. G., Moya Albor, E. y Brieva Rico, J. E. (editores). Challenges and Trends in Multimodal Fall Detection for Healthcare, (Studies in Systems, Decision and Control, vol. 273), (pp. v-vii). Cham, Switzerland : Springer. DOI: 10.1007/978-3-030-38748-8