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

Kong, Xiangbo; Chen, Lehan; Wang, Zhichen; Chen, Yuxi; Meng, Lin; Tomiyama, Hiroyuki

doi:10.3390/s19173768

Cited by 18 publications

(5 citation statements)

References 64 publications

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“…The maximum work for fall detection using DL have been done using CNN followed by hybrid, LSTM, Auto-encoder and MLP as shown in Figure 20. These DL based Classification of papers based on the CNN modle used CNN [39,41,44,20,52,45,54,59,61,63,64,65,66,67,68,8,69,71,73,74,76,77,78,79,80,81,82,84,87,88,89,90,91,36,92,93,94,95,99,100,101,102,103,104,107,105,108,106] LSTM [117,…”

Section: Discussion On Limitations and Future Scopementioning

confidence: 99%

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Vision-based Human Fall Detection Systems using Deep Learning: A Review

Alam¹,

Sufian²,

Dutta³

et al. 2022

Preprint

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Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-thearts non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques.

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Section: Discussion On Limitations and Future Scopementioning

confidence: 99%

“…Kong et al (2019) [63] showed how the height of the camera may affect the performance of the fall detection system. They used an enhanced tracking and denoising Alex-Net (ETDA-Net) which is basically an AlexNet with some pre-processing added to improve the tracking performance and to reduce the noise in the image.…”

Section: Cnn Based Techniquesmentioning

confidence: 99%

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Alam¹,

Sufian²,

Dutta³

et al. 2022

Preprint

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“…In [ 84 ], prior to input images in a CNN to generate feature maps, which will be used for classification, the background is subtracted through an algorithm that combines depth maps and 2D images to enhance segmentation performance. This way, if the pixels of the segmented 2D silhouette experiment sharp changes, but pixels in the depth map do not, pixels subject to those changes are regarded as noise.…”

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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“…A first group is formed by camera-based systems [15][16][17][18][19]. By means of a camera implemented in a room or on a person, a fall is detected based on an extensive algorithm that runs on a PC.…”

Section: Related Workmentioning

confidence: 99%

Bluetooth-Low-Energy-Based Fall Detection and Warning System for Elderly People in Nursing Homes

et al. 2022

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Due to the ever growing population of elderly people, there is a dramatic increase in fall accidents. Currently, multiple ideas exist to prevent the elderly from falling, by means of technology or individualised fall prevention training programs. Most of them are costly, difficult to implement or less used by the elderly, and they do not deliver the required results. Furthermore, the increasingly older population will also impact the workload of the medical and nursing personnel. Therefore, we propose a novel fall detection and warning system for nursing homes, relying on Bluetooth Low Energy wireless communication. This paper describes the hardware design of a fall-acceleration sensing wearable for the elderly. Moreover, the paper also focuses on a novel algorithm for real-time filtering of the measurement data as well as on a strategy to confirm the detected fall events, based on changes in the person’s orientation. In addition, we compare the performance of the algorithm to a machine learning procedure using a convolutional neural network. Finally, the proposed filtering technique is validated via measurements and simulation. The results show that the proposed algorithm as well as the convolutional neural network both results in an excellent accuracy when validating on a common database.

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

Cited by 18 publications

References 64 publications

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Vision-based Human Fall Detection Systems using Deep Learning: A Review

Comprehensive Review of Vision-Based Fall Detection Systems

Bluetooth-Low-Energy-Based Fall Detection and Warning System for Elderly People in Nursing Homes

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