Nowadays, the use of deep neural networks is growing in many problem domains, including vision-based fall detection. If a fall event occurs and the system does not detect it, the consequences can be dramatic: More importantly, cameras are also installed in elderly care centers.
Of the remaining papers, 31 did not attempt to evaluate their system based on accuracy, sensitivity or specificity of a detection device. These metrics are not biased by imbalanced class distributions, which make them more suitable for fall detection datasets where the number of fall samples is usually much lower than the number of nonfall samples.
There were no studies of non-wearable devices that used older adults as subjects in either a lab or a real world setting.
According to Ambrose et al. The whole architecture see Figure 3 was implemented using the Keras framework [ Vision based fall detection ] and is publicly available https: This ability could reduce the physical and mental damage caused not only by the fall but time after a fall before discovery.
Table 1 summarizes the most relevant figures of each dataset. These clinical alarm systems provide a way for individuals who fall to contact an emergency center by pressing a button.
In the FDD dataset, some falls are also far from the camera, although this is not a general case like in Multicam.
Article selection was conducted by the first author who reviewed full texts of the relevant articles using a data extraction spreadsheet developed for this review. Another common technique consists in computing the bounding boxes of the objects to determine if they contain a person and then detect the fall by means of features extracted from it see, for instance, [ 2021 ].
In this context, assistive devices that could help to alleviate this major health problem are a social necessity. These studies provided a general overview of the fall detection status, but it has changed greatly since they were published, and the current fall detection trends have little in common with those of previous years.
However, we believe that, with the irruption of the paradigm of the Internet of Things IoT [ 5 ], the possibilities to extend Smart Environments, and more specifically fall detection approaches, grow considerably.
Only studies including some experimental results or pioneering investigations have been considered. The most common metrics to assess the performance of such a classifier are sensitivity, also known as recall or true positive rate, and specificity or true negative rate.
The recent impact of deep learning has changed the landscape of computer vision, improving the results obtained in many relevant tasks, such as object recognition, segmentation, and image captioning [ 6 ]. Following a similar strategy, Vishwakarma et al. For instance, the Imagenet dataset, which is widely used for object recognition tasks in images, has 14 million images [ 7 ].
This is a standard practice in the deep learning literature [ 42 ], as the network learns generic features for image recognition; for example, it can distinguish corners, textures, basic geometric elements, and so on.
To address this problem, the optical flow algorithm [ 33 ] was used to describe the displacement vectors between two frames. In the case of falls, these measures are very different compared to daily activities or confounding events such as bending over or squattingallowing us to discern between them.
Related Work The literature of fall detection is divided between sensor-based and vision-based approaches. Many solutions are based on supervised learning, that is, extracting lots of features from raw images and using a classifier to learn a decision from labeled data. As the CNN has learned filters according to the optical flow images of the action recognition datasets, using the same configuration for the fall detection images minimizes the loss of performance due to the transfer learning.
Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. In a real-life scenario, they have the potential to mitigate some of the adverse consequences of a fall.
The use of optical flow images is also motivated by the fact that anything static background is removed and only motion is taken into account. The selection criteria were: The primary aim of this paper is to review the evidence on fall detection devices and to analyze their level of success in automatically detecting falls.
Optical flow images represent the motion of two consecutive frames, which is too short-timed to detect a fall. Inthe same author extended his previous work by adding Curvelet coefficients as extra features and applying a Hidden Markov Model HMM to model the different body poses [ 14 ]. Results This review identified 57 projects that used wearable systems and 35 projects using non-wearable systems, regardless of evaluation technique.
Fear of falling has been shown to be associated with negative consequences such as avoidance of activities, less physical activity, falling, depression, decreased social contact and lower quality of life [ 3 ].
The sensitivity is the ability of a detector to correctly classify a fall as a fall, while the specificity is the ability of a detector to correctly classify an ADL as ADL [ 13 ]. This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase.
Their main advantage is that the person does not need to wear any special device. The first part of our pipeline, the optical flow images generator, receives consecutive images and applies the TVL-1 optical flow algorithm [ 35 ].
Furthermore, it also pursues the generality of the learned features for different falling scenarios.Jul 06, · By contrast, Mubashir et al. divide fall detectors into three categories: wearable device based, ambience sensor based and camera (vision) based. Perry et al. [ 11 ] group them into three categories: methods that measure acceleration, methods that measure acceleration combined with other methods, and methods that do not measure acceleration.
Fall Detection Based on Body Part Tracking Using a Depth Camera vision-based method . Most of the fall detection methods based on vision try to execute in real-time using standard computers and low cost cameras.
The fall motion is very fast, body part tracking using a depth camera is proposed. To capture the fall motion, an improved. A Survey on Vision-based Fall Detection Zhong Zhang, Christopher Conly, and Vassilis Athitsos Department of Computer Science and Engineering University of Texas at Arlington.
sibilities to extend Smart Environments, and more speci cally fall detection approaches, grow considerably. In this paper we focus on vision-based approaches for fall detection.
In this paper, we propose a real-time computer vision–based system capable of automatically detecting falls of elderly persons in rooms, using a single camera of low cost and thus low resolution. reliability of the fall detection system, making it applicable to real world conditions.
This is. A major cause of deaths among the elderly relates to accidental falls. Such falls are of particular medical concern to this population because they often r.Download