Fall Detection Based on Depth-Data in Practice

Pramerdorfer, Christopher; Planinc, Rainer; Loock, Mark Van; Fankhauser, David; Kampel, Martin; Brandstötter, Michael

doi:10.1007/978-3-319-48881-3_14

Cited by 14 publications

(3 citation statements)

References 19 publications

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“…Many approaches based on computer vision have been proposed for fall detection. According to litterature, they can be classified as thresholding based methods [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ] and machine learning based methods [ 16 , 17 , 18 , 19 , 20 , 21 ]. The first technique to detect a fall is by extracting different features and setting a threshold.…”

Section: Related Workmentioning

confidence: 99%

“…The main concept is to recognize some abnormal posture of the person such as bending, sitting and lying, and then use some characteristics to check for the occurrence of the fall. In [ 12 ], the authors proposed an algorithm of fall detection in which three different states are identified: fall prediction, fall detection, and fall verification state. In the first state, the posture of the person that is tracked is identified.…”

Section: Related Workmentioning

confidence: 99%

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Fall Detection System-Based Posture-Recognition for Indoor Environments

2021

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The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fall Detection System-Based Posture-Recognition for Indoor Environments

2021

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“…Vision-based methods are considered promising [9]. Although such methods have been studied, many have limitations in terms of practicality [10]. Some methods [11,12,13,14] establish a background model and use an image-subtraction method to segment the individual then extract features of that individual to detect a fall.…”

Section: Introductionmentioning

confidence: 99%

Robust Self-Adaptation Fall-Detection System Based on Camera Height

Kong

Chen

Wang

et al. 2019

Sensors

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Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.

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