A cross-dataset deep learning-based classifier for people fall detection and identification

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A cross-dataset deep learning-based classifier for people fall detection and identification

机译：基于DataSet的跨数据集基于深度学习的分类器，用于人们跌倒检测和识别

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Background and Objective: Fall detection is an important problem for vulnerable sectors of the population such as elderly people, who frequently live alone. Note that a fall can be very dangerous for them if they cannot ask for help. Hence, in those situations, an automatic system that detected and informed to emergency services about the fall and subject identity could help to save lives. This way, they would know not only when but also who to help. Thus, our objective is to develop a new approach, based on deep learning, for fall detection and people identification that can be used in different datasets without any fine-tuning of the model parameters.

机译：背景和目的：跌倒检测对于弱势群体（如老年人）的脆弱部门，他们经常独自生活。请注意，如果他们不能要求帮助，他们对他们来说是非常危险的。因此，在这些情况下，一个检测到并告知堕落和主题身份的紧急服务的自动系统可以有助于挽救生命。这样，他们不仅会知道，而且还知道谁来帮助。因此，我们的目标是基于深度学习，开发一种新的方法，用于跌倒检测和人们识别，这些方法可以在不同的数据集中使用，而无需进行模型参数的任何微调。

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《Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice》 |2020年第2020期|共8页
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正文语种 eng
中图分类计算技术、计算机技术;
关键词
Fall detection; Activities of daily living; Inertial sensors; Convolutional neural network; Long short-term memory; Multi-task;

机译：跌倒检测;日常生活活动;惯性传感器;卷积神经网络;长期内存;多任务;

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1. A cross-dataset deep learning-based classifier for people fall detection and identification [J] . Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2020,第期

机译：基于DataSet的跨数据集基于深度学习的分类器，用于人们跌倒检测和识别
2. Segmentation techniques for early cancer detection in red blood cells with deep learning-based classifier—a comparative approach [J] . Shemona Jeya Sudharsan, Chellappan Agees Kumar Image Processing, IET . 2020,第9期

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3. Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers [J] . Shan Wang, Sulaiman Khan, Chuyi Xu, Complexity . 2020,第1期

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4. A Heterogeneous Ensemble Learning-Based Acoustic Fall Detection Method for Elderly People in Indoor Environment [C] . XiaoLing Li, JiaWei Li, JiaRui Lai, International Conference on Artificial Intelligence in HCI;International Conference on Human-Computer Interaction . 2020

机译：基于异质集合学习的室内老年人跌倒检测方法
5. Deep Neural Network Learning-Based Classifier Design for Big-Data Analytics [D] . Raghavan, Krishnan. 2019

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6. The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors Classifiers and Processing Conditions [O] . Bummo Koo, Jongman Kim, Yejin Nam, 2021

机译：使用Cross-DataSet的下降后检测性能：特征向量分类器和处理条件
7. Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers [O] . Shan Wang, Sulaiman Khan, Chuyi Xu, 2020

机译：基于深度学习的高效模型开发，用于使用随机林和BLSTM分类器的网络钓鱼检测

A cross-dataset deep learning-based classifier for people fall detection and identification

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