A fall detection system using k-nearest neighbor classifier

Liu, Chien‐Liang; Lee, Chia-Hoang; Lin, Ping-Min

doi:10.1016/j.eswa.2010.04.014

Cited by 174 publications

(70 citation statements)

References 11 publications

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“…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]). Following a similar strategy, Vishwakarma et al [22] worked with bounding boxes to compute the aspect ratio, horizontal and vertical gradients of an object, and fall angle and fed them into a GMM to obtain a final answer.…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos

Azkune

Arganda‐Carreras

2017

Wireless Communications and Mobile Computing

214

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%

Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos

Azkune

Arganda‐Carreras

2017

Wireless Communications and Mobile Computing

214

125

View full text Add to dashboard Cite

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“…The parameters of this shape are then used in SVM classification. This is a method much like those described in (Tao et al, 2005), (Yu et al, 2009) and (Liu et al, 2010), though these methods use a heuristic rule to determine if a fall took place, whereas we use an SVM classifier. We chose to use an SVM classifier in order to compare this method fairly with our own methods, which also use SVM classifiers.…”

Section: Overview Of Methodsmentioning

confidence: 99%

“…A fall is detected if the subject transitions from standing to bending to falling in a short period of time. A similar approach is described in (Liu et al, 2010), but instead of looking at an ellipse, the silhouette of the S. Battiato & J. Braz (Eds. ), Proceedings of the 8th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2013) (pp.…”

Section: Related Workmentioning

confidence: 99%

Fusion of Color and Depth Camera Data for Robust Fall Detection

Josemans¹,

Englebienne

Kröse

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:The availability of cheap imaging sensors makes it possible to increase the robustness of vision-based alarm systems. This paper explores the benefit of data fusion in the application of fall detection. Falls are a common source of injury for elderly people and automatic fall detection is, therefore, an important development in automated home care. We first evaluate a skeleton-based classification method that uses the Microsoft Kinect as a sensor. Next, we evaluate an overhead camera-based method that looks at bounding ellipse features. Then, we fuse the data from these two methods by validating the skeleton tracked by the Kinect. Data fusion proves beneficial, since the data fusion approach outperforms the other methods.

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“…Finally, a multi-frame Gaussian classifier is utilized to determine a fall event. Liu et al [20] use the frame differencing approach to identify the human body. Then, image processing techniques are applied to smooth the input.…”

Section: Related Workmentioning

confidence: 99%

Automatic Fall Detection using Smartphone Acceleration Sensor

Dang¹,

Truong²,

Dang³

2016

ijacsa

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Abstract-In this paper, we describe our work on developing an automatic fall detection technique using smart phone. Fall is detected based on analyzing acceleration patterns generated during various activities. An additional long lie detection algorithm is used to improve fall detection rate while keeping false positive rate at an acceptable value. An application prototype is implemented on Android operating system and is used to evaluate the proposed technique performance. Experiment results show the potential of using this app for fall detection. However, more realistic experiment setting is needed to make this technique suitable for use in real life situations.

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A fall detection system using k-nearest neighbor classifier

Cited by 174 publications

References 11 publications

Vision-Based Fall Detection with Convolutional Neural Networks

Vision-Based Fall Detection with Convolutional Neural Networks

Fusion of Color and Depth Camera Data for Robust Fall Detection

Automatic Fall Detection using Smartphone Acceleration Sensor

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