Survey of fall detection and daily activity monitoring techniques

Hijaz, Farrukh; Afzal, Nabeel; Ahmad, Talal; Hasan, Osman

doi:10.1109/iciet.2010.5625702

Cited by 50 publications

(32 citation statements)

References 20 publications

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“…As a consequence of falls, an individual may be harmed both physiologically and psychologically. This can be in the form of an injury of various types, a limited activity, and an uneasiness of living independently and fear of falling [4]. Thus, reducing the risk of a person's harm from fall comes at the top of the safety goals for developed and developing countries.…”

Section: Related Workmentioning

confidence: 99%

“…Accidental falls are seriously harmful events for the older persons and more than 33% of people aged 65 years and above experience one fall every year [4]. As a consequence of falls, an individual may be harmed both physiologically and psychologically.…”

Section: Related Workmentioning

confidence: 99%

“…Here comes the importance of designing and implementing an automatic Fall Detection System that detects the fall of unattended older persons and sends an alarm message to the concerned caregivers, to provide the needed help in the shortest delay possible. Fall detection systems can be classified into three main approaches [4]: (1) Computer vision and image processing based approach, in which the movement of the person is monitored in real time through a video capturing system and some algorithms are applied to recognize the posture of the person [5] [16]. In each of the above mentioned approaches, different types of sensing modules, decision making modules, and alarm modules are used.…”

Section: Related Workmentioning

confidence: 99%

“…[3]. Existing fall detection systems are still having several challenges such as limited range of operation being restricted to indoor environment, need of expensive infrastructure for reliable detection, and obtrusiveness of the detection system as the individual has to wear special setting of sensors [4].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

RAFDS: Remote Abnormality and Fall Detection System for Assisting Older Persons

Ismail¹,

Kallel²,

Ismail³

2015

International Journal of Advanced Research in Computer and Comm

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Abstract:One of the main problems facing the public health is the injury that happens due to older persons falling, as these injuries can be fatal for them. Fast and proper medical interventions are crucial to reduce the serious consequences on their health. In this paper, a system is designed to continuously monitor vital signs and motion of older persons, and automatically detects incidents of abnormal vital signs as well as the fall of unattended older persons, and, accordingly, sends an alarm message to the concerned caregivers, to provide the needed help in the shortest delay possible. The proposed system is composed of three major modules; firstly the sensing module (SM), which continuously measures the acceleration of the monitored person, as well as vital signs of the monitored persons, such as the temperature and the heart pulse rate. Secondly, the decision making module (DMM) which is a processing element that receives the sensing signals from the sensors used in SM, analyzes them, then makes a decision if an abnormality or a fall is detected or not. Finally, if the DMM detects a fall, it sends this decision signal to the alarm module (AM), which sends SMS messages to the concerned caregivers, whose cell phone numbers, are pre-configured in the AM.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RAFDS: Remote Abnormality and Fall Detection System for Assisting Older Persons

Ismail¹,

Kallel²,

Ismail³

2015

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

show abstract

“…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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