An Image-Based Fall Detection System for the Elderly

Lu, Kun-Lin; Chu, Edward T.-H.

doi:10.3390/app8101995

Cited by 31 publications

(16 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This algorithm, integrated into OpenCV and used in [ 59 ], first converts images RGB to hue-saturation-value (HSV) and, starting with frames where a CNN has created a bounding box (BB) around a detected person, it determines the hue histogram in each BB. Then, morphological operations are applied to reduce noise associated with illumination.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Review of Vision-Based Fall Detection Systems

Gutiérrez

Rodríguez²,

Martín

2021

Sensors

View full text Add to dashboard Cite

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers.

show abstract

Section: Discussionmentioning

confidence: 99%

Comprehensive Review of Vision-Based Fall Detection Systems

Gutiérrez

Rodríguez²,

Martín

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…One study that has basis development by using a conventional device (Soewito et al, 2015) which originated from Indonesia and one study from Brazil (Rodrigues et al, 2018). A total of two studies conducted in Japan (Kong et al, 2018;Sumiya et al, 2015) practice image analysis in detecting fall events and one study originated from Taiwan (Lu & Chu, 2018).…”

Section: General Findings and Characteristics Of The Study Included In The Reviewmentioning

confidence: 99%

Classification of Fall Detection System for Elderly: Systematic Review

et.al¹

2021

TURCOMAT

View full text Add to dashboard Cite

Elderly are the world’s largest growing population, categorized over the age of 60 to 65 years. They are the ones who prone to fall due to their old age and low self-efficacy, thus making them vulnerable to different accidents. Even doing daily activities can also expose the elderly to a fall incident. As a result, it has gained the attention of many researchers in conducting studies related to the elderly daily health care, especially in relation to the fall detection system. This paper aims to provide a systematic review on the classification of fall detection systems for the elderly. This systematic review is designed based on the existing and extensive literature review on fall detection systems guided by the prisma statement (preferred reporting items for systematic reviews and meta-analyses) review method. Based on this systematic review, four overarching themes that provide in-depth information on fall detection to detect fall events have been identified; classification of fall detection, basis development, type of sensor and detection technique. In a nutshell, the fall detection approach has successfully provided an alternative health care services for elderly who choose to live independently. Therefore, it is important to continue to develop a fall detection system that integrates with technology in order to provide a safe living environment for elderly, and for children, it can offer as an alternative for monitoring systems.

show abstract

“…[18] propose a method for human posture-based and movements-based monitoring, limited however to only 5 postures (standing, bending, sitting, lying and lying toward) and 4 movements (running, jump, inactive, active). IFADS (Image-based FAll Detection System) [19] focuses on falls that might happen while sitting down and standing up from a chair, a situation of potential danger for elderly people.…”

Section: Ambient Assisted Living Systemsmentioning

confidence: 99%

A Vision-Based System for Monitoring Elderly People at Home

2020

View full text Add to dashboard Cite

Assisted living technologies can be of great importance for taking care of elderly people and helping them to live independently. In this work, we propose a monitoring system designed to be as unobtrusive as possible, by exploiting computer vision techniques and visual sensors such as RGB cameras. We perform a thorough analysis of existing video datasets for action recognition, and show that no single dataset can be considered adequate in terms of classes or cardinality. We subsequently curate a taxonomy of human actions, derived from different sources in the literature, and provide the scientific community with considerations about the mutual exclusivity and commonalities of said actions. This leads us to collecting and publishing an aggregated dataset, called ALMOND (Assisted Living MONitoring Dataset), which we use as the training set for a vision-based monitoring approach.We rigorously evaluate our solution in terms of recognition accuracy using different state-of-the-art architectures, eventually reaching 97% on inference of basic poses, 83% on alerting situations, and 71% on daily life actions. We also provide a general methodology to estimate the maximum allowed distance between camera and monitored subject. Finally, we integrate the defined actions and the trained model into a computer-vision-based application, specifically designed for the objective of monitoring elderly people at their homes.

show abstract

An Image-Based Fall Detection System for the Elderly

Cited by 31 publications

References 36 publications

Comprehensive Review of Vision-Based Fall Detection Systems

Comprehensive Review of Vision-Based Fall Detection Systems

Classification of Fall Detection System for Elderly: Systematic Review

A Vision-Based System for Monitoring Elderly People at Home

Contact Info

Product

Resources

About