Human-fall detection from an indoor video surveillance

Agrawal, Subhash Chand; Tripathi, Ramesh Kumar; Jalal, Anand Singh

doi:10.1109/icccnt.2017.8203923

Cited by 30 publications

(21 citation statements)

References 14 publications

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“…Specifically, we compared our model with the model in [13], which achieved accuracy 0.95. The model in [18] had accuracy 0.94, while the [20] approach reached accuracy 0.91. The proposed model in [18] has an accuracy 0.86, while the [25] model has an accuracy 0.96.…”

Section: Resultsmentioning

confidence: 99%

“…Human fall detection is inferred by a precise method based on indoor visual surveillance. Such a system incorporates the Gaussian mixture model (GMM) to exploit the foreground objects to perform pattern recognition [18]. Detection of falls by elderly persons is achieved by using a floor sensor, thereby enhancing electric near field imaging [19].…”

Section: Introductionmentioning

confidence: 99%

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Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Kyriakopoulos

Ntanos

Anagnostopoulos

et al. 2020

IJERPH

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Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Kyriakopoulos

Ntanos

Anagnostopoulos

et al. 2020

IJERPH

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“…Mirmahboub et al [27] used two different background separation methods to find a human silhouette and used the area of the silhouette as a feature to feed into a support vector machine (SVM) for classification. Agrawal et al [28] used background subtraction to find objects in the foreground and categorized a human by contour-based human template matching. They detected a fall by computing the distance between the top and mid center of the bounding box of a human.…”

Section: Image-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAll Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.

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“…Depending on the techniques used, we can find systems for monitoring people and/or detect falls that use acoustic sensors [6][7][8][9], video camera and image processing [10][11][12][13] or even sensors attached to the human body [14][15][16].…”

Section: State Of the Artmentioning

confidence: 99%

System for Monitoring People with Disabilities in the Event of an Accident using Mobile Terminals

Fanca¹,

Cujerean²,

Puscasiu³

et al. 2019

IJACSA

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Being in the speed century, people around the world are busy scheduling every day and therefore, it is impossible to spend enough time with the elderly and people with disabilities or people who have a chronic illness. These persons need a lot more attention and care because they cannot cope with the daily activities like a healthy person would. Daily monitoring and assistance of elderly or disabled people is a very important task, both in the current activity, or, especially when emergencies can occur. Fortunately, we can easily use today's technologies, which are constantly developing to be able to monitor them remotely. This paper tries to find a solution to reduce cases of fatality due to accidents, by using advanced technologies of today, e.g. smartphones, fast communications. The use of these technologies can provide permanent monitoring of the elderly and persons with disabilities, without bounding their mobility and without affecting their quality of life. In this way, if emergency situations arise for the elderly or people with disabilities or chronic diseases, measures can be taken as soon as possible. The development of a mobile application capable to monitor the occurrence of accidents for the above-mentioned persons is obviously a help granted to the doctors involved in ensuring their health. Thus, the main objective of the application is to detect the accidental falls of the persons in the shortest possible time. Another objective is to provide an application that runs in the background of the mobile operating system, using as little as possible the power supply.

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Human-fall detection from an indoor video surveillance

Cited by 30 publications

References 14 publications

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

An Image-Based Fall Detection System for the Elderly

System for Monitoring People with Disabilities in the Event of an Accident using Mobile Terminals

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