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Fall Detection Using Still Images in Hybrid Classifier

Kandavel, Srianuradha
http://orcid.org/0000-0002-2477-169X
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637150174049626

Abstract Details

2021, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
According to a report produced by WHO in 2019, 1 in 11 people globally are considered elderly (over 65 years of age) accounting to more than 703 million elderly people. This calls for innovative prevention of quality livelihood in the elderly. The increase in older population results in careful monitoring of their daily activities. Fall related injuries and death has estimated about 30% of deaths in the USA. About 20-30% of elderly suffer from medium to severe injuries due to fall. So, monitoring and timely reporting the injuries is of utmost importance. Within this context, fall detection becomes a prominent field of study to cater better to the incapacitated elders. There are many devices and techniques commercially available to monitor their daily activities. Development in machine learning and deep learning has paved way for using artificial networks in fall detection. Video surveillance when combined with image processing has proven to be more effective than wearable devices. However, this will cost more memory space, computation, and money. Fall detection in still images is a conservative alternative but it is still under explored. Convolutional Neural Networks (CNN) are efficiently used in image processing. CNN can be used to get features from images. In this research work, VGG19 Network has been used to extract features from images. These feature maps are then used to classify the fall action using a KNN classifier. Dataset of depth images with a mixture of fall actions and daily activities has been used for this research purpose. This combination of classifiers has shown a result of approximately 60 % accuracy when compared to the individual classifiers.
Anca Ralescu, Ph.D. (Committee Chair)
Dan Ralescu (Committee Member)
Kenneth Berman, Ph.D. (Committee Member)
46 p.
Computer Science
Fall detection; Hybrid classifier; Machine Learning; Image Classification

Recommended Citations

Citations

  • Kandavel, S. (2021). Fall Detection Using Still Images in Hybrid Classifier [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637150174049626

    APA Style (7th edition)

  • Kandavel, Srianuradha. Fall Detection Using Still Images in Hybrid Classifier. 2021. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637150174049626.

    MLA Style (8th edition)

  • Kandavel, Srianuradha. "Fall Detection Using Still Images in Hybrid Classifier." Master's thesis, University of Cincinnati, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1637150174049626

    Chicago Manual of Style (17th edition)

ucin1637150174049626
133
© 2021, some rights reserved.Creative Commons License Fall Detection Using Still Images in Hybrid Classifier by Srianuradha Kandavel is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.