Feature selection to detect fallen pose using depth images

Maldonado, Carolina Solís; Ríos, Homero; Mezura-Montes, Efrén; Marín, Antonio

doi:10.1109/conielecomp.2016.7438558

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…In [ 42 ], silhouettes are tracked by using a proportional-integral-differential (PID) controller. A bounding box is created around the silhouette, and features are extracted in accordance with [ 131 ]. A fall will be called if thresholds established for features are exceeded.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Review of Vision-Based Fall Detection Systems

Gutiérrez

Rodríguez²,

Martín

2021

Sensors

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Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Review of Vision-Based Fall Detection Systems

Gutiérrez

Rodríguez²,

Martín

2021

Sensors

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“…Furthermore, for the skeleton-based fall detection related works [11,30,31,34], they used either a fixed set or all of the skeleton joints; however, none of them tried to investigate the joints which are most effective in detecting various types of fall scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…Tower this end, several recent studies [11,31,32,34] used skeleton joints to detect different scenarios of fall. C. Lee and V. Lee [11] use the Kinect v1 camera as the input sensor and Microsoft Kinect SDK to get skeleton data for their FD system.…”

Section: Fall Detection Using Skeleton Joints and Featuresmentioning

confidence: 99%

“…The most recent related work in fall features was published in 2016 by Maldonado et al [34]. The authors investigated feature selection to identify the features that distinguish most between fallen and non-fallen poses.…”

Section: Fall Detection Using Skeleton Joints and Featuresmentioning

confidence: 99%

“…Overall, the above works used either a fixed set or all of the skeleton joints; however, none of them tried to determine the joints which are most effective in detecting various types of fall scenarios. Maldonado et al [34] tested some features that can be extracted from depth data (not skeleton data). Researchers must investigate the 25 skeleton joints among those offered by Kinect v2 (SDK 2.0) as in Figure 4.b and their features that most efficiently could detect various fall scenarios.…”

Section: Fall Detection Using Skeleton Joints and Featuresmentioning

confidence: 99%

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Computer Vision Based Fall Detection Methods Using the Kinect Camera : A Survey

Jarraya

2018

IJCSIT

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Disabled people can overcome their disabilities in carrying out daily tasks in many facilities [1]. However, they frequently report that they experience difficulty being independently mobile. And even if they can, they are likely to have some serious accidents such as falls. Furthermore, falls constitute the second leading cause of accidental or injury deaths after injuries of road traffic which call for efficient and practical/comfortable means to monitor physically disabled people in order to detect falls and react urgently. Computer vision (CV) is one of the computer sciences fields, and it is actively contributing in building smart applications by providing for image\video content "understanding." One of the main tasks of CV is detection and recognition. Detection and recognition applications are various and used for different purposes. One of these purposes is to help of the physically disabled people who use a cane as a mobility aid by detecting the fall. This paper surveys the most popular approaches that have been used in fall detection, the challenges related to developing fall detectors, the techniques that have been used with the Kinect in fall detection, best points of interest (joints) to be tracked and the well-known Kinect-Based Fall Datasets. Finally, recommendations and future works will be summarized.

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