An Improvement in Fall Detection System by Voting Strategy

Jariyavajee, Chattriya; Faphatanchai, Athicom; Saeheng, Wiroat; Tuntithawatchaikul, Chutichai; Sirinaovakul, Booncharoen; Polvichai, Jumpol

doi:10.1109/itc-cscc.2019.8793440

Cited by 2 publications

(2 citation statements)

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“…The procedures vision-based systems used are more or less the same as these following steps: (i) image preprocessing, (ii) background subtraction or foreground segmentation, (iii) feature extraction, and (iv) event recognition [54]. The dominate difference among different approaches is the third step on what kind of feature is determined and extracted.…”

Section: Systems Based On Image Processingmentioning

confidence: 99%

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Possible Life Saver: A Review on Human Fall Detection Technology

et al. 2020

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Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

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Section: Systems Based On Image Processingmentioning

confidence: 99%

“…A KNN algorithm is used to predict a fall with an accuracy of 96.9%. In [54], three depth cameras were used to work independently. The subject's body contour was used as the foreground feature which is analyzed and labeled as fall or non-fall events by each camera source.…”

Section: Systems Based On Image Processingmentioning

confidence: 99%