Titlebook: Challenges and Trends in Multimodal Fall Detection for Healthcare; Hiram Ponce,Lourdes Martínez-Villase?or,Ernesto Mo Book 2020 Springer N

悄悄移動(dòng)

Open Source Implementation for Fall Classification and Fall Detection Systemsment of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate be

Bone-Scan

Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Tedirectly, to society’s productivity. Unsurprisingly, human fall detection and prevention is a major focus of health research. In this chapter, we present and evaluate several bidirectional long short-term memory (Bi-LSTM) models using a data set provided by the Challenge UP competition. The main goa

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Highbrow

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Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning te

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Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an Inence on Neural Networks (IJCNN 2019). This competition lies on the fall classification problem, and it aims to classify eleven human activities (i.e. five types of falls and six simple daily activities) using the joint information from different wearables, ambient sensors and video recordings, store