Vision-Based Fall Detection Using ST-GCN

doi:10.1109/ACCESS.2021.3058219

2021-01-01

Oussema Keskes *

Rita Noumeir

DOI：10.1109/ACCESS.2021.3058219

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摘要

En 中文

Falls are a growing issue in society and has become a hot topic in the healthcare domain. Falls are more likely to occur to due to age or health problems such as cardiovascular issues and muscle weakness. In this work we focus on fall detection. The aftereffects of falls often lead to the use of prescription pain medications. We are motivated to help prevent suicide attempts by overdose in the Canadian correctional services. Most previous studies were based on hand-crafted features which limit the robustness and generality of the system. We therefore propose a general vision-based system, using Spatial Temporal Graph Convolutional Networks (ST-GCN). This system has proven its efficiency and robustness in the action recognition domain. Contrary to previous works, this model can be applied directly to new data without the need to retrain the model while offering good accuracy. Additionally, with the help of transfer learning we can solve the insufficient data problem. By using three public datasets: the NTU RGB-D dataset, the TST Fall detection dataset v2 and the Fallfree dataset to validate our method, we achieved a 100% accuracy, surpassing the state-of-the-art.

Keyword:

Fall detection

Accelerometers

Sensors

Feature extraction

Transfer learning

Deep learning

Cameras

Suicide prevention

unconsciousness

fall detection

deep learning

ST-GCN

RGB-D data

skeleton

TST fall detection dataset v2

fallfree

NTU RGB-D

期刊

IEEE Access

IF:

3.6

论文数:

9.8W

被引数:

29.4W

机构

university of quebec

学者数:

2.0W

论文数: 1.9W

被引数: 19

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