Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy

Xi, Cai; An, Mingyue; Han, Guoqiang

doi:10.1109/access.2021.3054469

Cited by 15 publications

(5 citation statements)

References 32 publications

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“…In [ 23 ], the authors presented a dense block-based drop detection method with a multi-channel convolutional fusion (MCCF) strategy. This method shows excellent results for fall detection over the same dataset (F-score of 0.973).…”

Section: Fall Detection Datasets and Related Workmentioning

confidence: 99%

Human Activity Recognition by Sequences of Skeleton Features

Ramirez

Velastín

Aguayo

et al. 2022

Sensors

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In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people’s activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.

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Section: Fall Detection Datasets and Related Workmentioning

confidence: 99%

Human Activity Recognition by Sequences of Skeleton Features

Ramirez

Velastín

Aguayo

et al. 2022

Sensors

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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%

“…Cai et al ( 2021) [105] introduced a multi-channel convolutional fusion (MCCF) based fall detection method using dense blocks and transition layers. At first, input frames (10) were fed to the convolutional layer.…”

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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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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“…2, many researchers have attempted to develop contactless monitoring systems for various applications. Previous methods for monitoring people can be divided into 1) vision-based [4][5][6][7][8][9][10][11][12][13], 2) wearable-based [14][15][16][17][18][19][20][21], and 3) head-gaze-based technologies [22][23][24][25][26][27][28][29][30][31]. Vision-based methods usually detect and track the pose or movements of medically vulnerable people using user images captured by cameras.…”

Section: Figure 1: Examples Of Medically Vulnerable Peoplementioning

confidence: 99%

“…These methods mainly focus on fall detection, behavior monitoring, and analysis of sleep patterns. First, [4][5][6][7] proposed a fall detection system based on user images. These systems observed user movements by capturing RGB and depth images in an indoor environment with bright lighting conditions.…”

Section: Vision-based Monitoringmentioning

confidence: 99%

Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

Kim¹,

Jeong²

2022

Computers, Materials &Amp; Continua

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Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems. However, previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images. This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics: 1) users' continuous movements, 2) various lighting conditions, and 3) a limited amount of available data. To address these issues, we introduce a multi-view multi-modal head-gaze estimation system that translates the user's head orientation into the gaze direction. The proposed system captures the user using multiple cameras with depth and infrared modalities to train more robust gaze estimators under the aforementioned conditions. To this end, we implemented a deep learning pipeline that can handle different types and combinations of data. The proposed system was evaluated using the data collected from 10 volunteer participants to analyze how the use of single/multiple cameras and modalities affect the performance of head-gaze estimators. Through various experiments, we found that 1) an infrared-modality provides more useful features than a depth-modality, 2) multi-view multi-modal approaches provide better accuracy than singleview single-modal approaches, and 3) the proposed estimators achieve a high inference efficiency that can be used in real-time applications.

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Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy

Cited by 15 publications

References 32 publications

Human Activity Recognition by Sequences of Skeleton Features

Human Activity Recognition by Sequences of Skeleton Features

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

Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

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