Survey and evaluation of real-time fall detection approaches

Perry, James; Kellog, Scott; Vaidya, Sundar M.; Youn, Jong-Hoon; Ali, Hesham; Sharif, Hamid

doi:10.1109/honet.2009.5423081

Cited by 79 publications

(51 citation statements)

References 15 publications

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“…Two years later, Perry et al [8] reported existing fall detection methods that have been found effective by others, as well as documented the findings of their experiments, the combination of which would assist in the progression towards a safe, unobtrusive monitoring system for independent seniors. In 2013, Mubashir et al [6] gave a survey on different systems for fall detection and their underlying algorithms, and divided fall detection approaches into three main categories: wearable device based, ambience device based and vision based.…”

Section: The Related Work On Prior Surveysmentioning

confidence: 99%

New Advances and Challenges of Fall Detection Systems: A Survey

2018

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Falling, as one of the main harm threats to the elderly, has drawn researchers' attentions and has always been one of the most valuable research topics in the daily health-care for the elderly in last two decades. Before 2014, several researchers reviewed the development of fall detection, presented issues and challenges, and navigated the direction for the study in the future. With smart sensors and Internet of Things (IoT) developing rapidly, this field has made great progress. However, there is a lack of a review and discussion on novel sensors, technologies and algorithms introduced and employed from 2014, as well as the emerging challenges and new issues. To bridge this gap, we present an overview of fall detection research and discuss the core research questions on this topic. A total of 6830 related documents were collected and analyzed based on the key words. Among these documents, the twenty most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance. The findings would assist researchers in understanding current developments and barriers in the systems of fall detection. Although researchers achieve fruitful work and progress, this research domain still confronts challenges on theories and practice. In the near future, the new solutions based on advanced IoT will sustainably urge the development to prevent falling injuries.

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Section: The Related Work On Prior Surveysmentioning

confidence: 99%

New Advances and Challenges of Fall Detection Systems: A Survey

2018

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“…However, a mobile phone is rarely held by elderly people during indoor ADL. Perry et al [46] present a survey on real-time fall detection methods based on techniques that measure only acceleration, techniques that combine acceleration with other methods, and techniques that do not measure acceleration. They conclude that the methods Khan et al [9] present a taxonomy of fall detection techniques based on two high-level categories from the data availability perspective: (I) sufficient training data for falls; and (II) insufficient or no training data for falls.…”

Section: Ambient Assistive Technology For Indoor Fall Riskmentioning

confidence: 99%

Ambient Technology to Assist Elderly People in Indoor Risks

Yared

Abdulrazak

2016

Computers

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While elderly people perform their daily indoor activities, they are subjected to several risks. To improve the quality of life of elderly people and promote healthy aging and independent living, elderly people need to be provided with an assistive technology platform to rely on during their activities. We reviewed the literature and identified the major indoor risks addressed by assistive technology that elderly people face during their indoor activities. In this paper, we identify these risks as: fall, wrong self-medication management, fire, burns, intoxication by gas/smoke, and the risk of inactivity. In addition, we discuss the existing assistive technology systems and classify the risk detection algorithms, techniques and the basic system principles and interventions to enhance safety of elderly people.

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“…An automatic fall detection system which does not need user intervention can overcome this problem. Because of the importance of this issue, a lot of research has been done to solve the fall detection challenge, as can be seen in the numerous available review articles [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. There are different ways to detect a fall; Mubashir et al categorizes them, for example, in three categories: wearable sensors, vision, and ambient/fusion [14].…”

Section: Introductionmentioning

confidence: 99%

Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

et al. 2017

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More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.

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Survey and evaluation of real-time fall detection approaches

Cited by 79 publications

References 15 publications

New Advances and Challenges of Fall Detection Systems: A Survey

New Advances and Challenges of Fall Detection Systems: A Survey

Ambient Technology to Assist Elderly People in Indoor Risks

Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

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