Fall detection - Principles and Methods

Noury, Norbert; Fleury, Anthony; Rumeau, Pierre; Bourke, Alan K.; Laighin, Gearóid Ó; Rialle, Vincent; Lundy, Jean-Eric

doi:10.1109/iembs.2007.4352627

Cited by 477 publications

(298 citation statements)

References 20 publications

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“…The first one is to place wearable sensors on the subject and detect falls with acceleration or rotation information. A detailed survey of this type of methodology is proposed by Noury et al [7]. But these kinds of measure are intrusive in the subject's daily life.…”

Section: Fall Detection Systemsmentioning

confidence: 99%

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Fall Detection from Depth Map Video Sequences

Rougier

Auvinet

Rousseau

et al. 2011

Lecture Notes in Computer Science

136

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Abstract. Falls are one of the major risks for seniors living alone at home. Computer vision systems, which do not require to wear sensors, offer a new and promising solution for fall detection. In this work, an occlusion robust method is presented based on two features: human centroid height relative to the ground and body velocity. Indeed, the first feature is an efficient solution to detect falls as the vast majority of falls ends on the ground or near the ground. However, this method can fail if the end of the fall is completely occluded behind furniture. Fortunately, these cases can be managed by using the 3D person velocity computed just before the occlusion.

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Section: Fall Detection Systemsmentioning

confidence: 99%

“…Falls can be detected during the post-fall phase when the person is motionless on the ground just after the fall [7]. The centroid height of the person relative to the ground has been used to detect an abnormal position near the floor [5].…”

Section: Fall Detectionmentioning

confidence: 99%

Fall Detection from Depth Map Video Sequences

Rougier

Auvinet

Rousseau

et al. 2011

Lecture Notes in Computer Science

136

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“…Fall can be described as an unintentional or sudden change of position of the body from an upright, sitting or lying position to a lower inclining position as defined in the paper [1]. A fall is an event that results in a person coming to rest on the ground or any lower level unintentionally [2].…”

Section: Introductionmentioning

confidence: 99%

Activity recognition for indoor fall detection using convolutional neural network

Adhikari

Bouchachia

Nait-Charif

2017

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)

108

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Falls are a major health problem in the elderly population. Therefore, a dedicated monitoring system is highly desirable to improve independent living. This paper presents a video based fall detection system in an indoor environment using convolution neural network. Identifying human poses is important in detecting fall events as specific "change of pose" defines a fall. Knowledge of series of poses is a key to detecting fall or non-fall events. A lying pose which may be considered as an after-fall pose is different from other normal activities such as lying/sleeping on the sofa or crawling. This paper uses Convolutional Neural Networks (CNN) to recognise different poses. Using Kinect, the following image combinations are explored: RGB, Depth, RGB-D and background subtracted RGB-D. We have constructed our own dataset by recording different activities performed by different people in different indoor set-ups. Our results suggest that combining RGB background subtracted and Depth with CNN gives the best possible solution for monitoring indoor video based falls.

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“…Noury et al [3] and Yu et al [4] have investigated the principles of fall detection and reviewed early works on the subject. Fall detection approaches can be divided into two main categories: vision-based and wearable device (motion sensor)-based systems.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Sensor Placement for Accurate Fall Detection

Ntanasis

Pippa

Özdemir

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations.

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Fall detection - Principles and Methods

Cited by 477 publications

References 20 publications

Fall Detection from Depth Map Video Sequences

Fall Detection from Depth Map Video Sequences

Activity recognition for indoor fall detection using convolutional neural network

Investigation of Sensor Placement for Accurate Fall Detection

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