A survey on fall detection: Principles and approaches

Mubashir, Muhammad; Shao, Ling; Seed, N. L.

doi:10.1016/j.neucom.2011.09.037

Cited by 735 publications

(405 citation statements)

References 46 publications

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“…Applying a matching system along the video to track the deformation of the silhouette, they analyzed the shape of the body and finally obtained a result with a GMM. Mubashir et al [3] tracked the person's head to improve their base results using a multiframe Gaussian classifier, which was fed with the direction of the principal component and the variance ratio of the silhouette. 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, [20,21]).…”

Section: Related Workmentioning

confidence: 99%

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Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos

Azkune

Arganda‐Carreras

2017

Wireless Communications and Mobile Computing

215

125

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One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. 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. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. 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. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

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Section: Related Workmentioning

confidence: 99%

“…The most common strategies consist in a combination of sensing and computing technologies to collect relevant data and develop algorithms that can detect falls based on the collected data [3]. These approaches have led to the appearance of Smart Environments for elderly assistance, which had been traditionally limited to home settings [4].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos

Azkune

Arganda‐Carreras

2017

Wireless Communications and Mobile Computing

215

125

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“…It is not an exhaustive summary but a representative list for demonstration of different sensor modality usage. One prominent area of application is fall detection reviewed in [8], using wearable, ambient and camera based approaches. Accurate localisation within the home environment is an important component in AAL applications [9].…”

Section: Home Environment Monitoringmentioning

confidence: 99%

“…Recently RGB-Depth (RGB-D) devices have been used successfully for this task, which outperform other sensing technologies [8]. The use of staircases can directly reflect musculoskeletal problems and the progress of recovery, and more recently, a general method for online estimation of the quality of movement on stairs was proposed in [18].…”

Section: Vision-based Monitoringmentioning

confidence: 99%

Bridging e-Health and the Internet of Things: The SPHERE Project

et al. 2015

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“…Ziebart et al predicts people's future locations [34] and Kitani et al [12] forecasts human actions by considering the physical environment. Other works involving daily activities include daily action classification or summarization by egocentric videos [7,14,17], fall detection [15], and classification of cooking actions [11,21,23,26].…”

Section: Related Workmentioning

confidence: 99%

Snippet Based Trajectory Statistics Histograms for Assistive Technologies

İşcen

Wang

Duygulu

et al. 2015

Computer Vision - ECCV 2014 Workshops

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Abstract. Due to increasing hospital costs and traveling time, more and more patients decide to use medical devices at home without traveling to the hospital. However, these devices are not always very straight-forward for usage, and the recent reports show that there are many injuries and even deaths caused by the wrong use of these devices. Since human supervision during every usage is impractical, there is a need for computer vision systems that would recognize actions and detect if the patient has done something wrong. In this paper, we propose to use Snippet Based Trajectory Statistics Histograms descriptor to recognize actions in two medical device usage problems; inhaler device usage and infusion pump usage. Snippet Based Trajectory Statistics Histograms encodes the motion and position statistics of densely extracted trajectories from a video. Our experiments show that by using Snippet Based Trajectory Statistics Histograms technique, we improve the overall performance for both tasks. Additionally, this method does not require heavy computation, and is suitable for real-time systems.

show abstract

A survey on fall detection: Principles and approaches

Cited by 735 publications

References 46 publications

Vision-Based Fall Detection with Convolutional Neural Networks

Vision-Based Fall Detection with Convolutional Neural Networks

Bridging e-Health and the Internet of Things: The SPHERE Project

Snippet Based Trajectory Statistics Histograms for Assistive Technologies

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