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Real-time Vision-based Fall Detection : with Motion History Images and Convolutional Neural Networks

Falls among the elderly is a major health concern worldwide due to theserious consequences, such as higher mortality and morbidity. And as theelderly are the fastest growing age group, an important challenge for soci-ety is to provide support in their every day life activities. Given the socialand economical advantages of having an automatic fall detection system,these systems have attracted the attention from the healthcare industry.With the emerging trend of Smart Homes and the increasing numberof cameras in our daily environments, this creates an excellent opportu-nity for vision-based fall detection systems. In this work, an automaticreal-time vision-based fall detection system is presented. It uses motionhistory images to capture temporal features in a video sequence, spatialfeatures are then extracted efficiently for classification using depthwiseconvolutional neural network. The system is evaluated on three publicfall detection datasets, and furthermore compared to other state-of-the-art approaches.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-71137
Date January 2018
CreatorsHaraldsson, Truls
PublisherLuleå tekniska universitet, Institutionen för system- och rymdteknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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