| dc.description.abstract | The home environment is where accidents frequently occur for children, and children typically spend most of their time in the home and kindergarten environment. Therefore, ensuring a safe home is critical to the safety of children, and falls are the most common accident. At present, the existing methods for children’s danger detection are mainly based on wearable sensors, which have a single function and are inconvenient to use. In addition, the existing deep learning methods for fall detection in the home environment are relatively few, and there are still many directions worth exploring.
Therefore, this paper proposes a monitoring system for children’s dangerous behaviors based on deep learning technology, which aims to recognize children’s
actions and detect falls in real time. The system divides the children’s posture into five categories: standing posture, lying up, lying down, sitting posture and falling posture. Among them, falling is regarded as the most important action. When the system detects the occurrence of a fall, it will quickly send an alarm or notify the parents or guardians.
Since there is no publicly available children’s action data set, this article collects real children’s videos on the Internet, a total of 1006 videos, Contains videos for children from various perspectives in the home environment. And with continuous images as input, using technologies such as deep learning, image processing algorithms, and skeleton detection, It can recognize the actions of children and further detect the occurrence of falls.
The accuracy of this system in motion recognition is 89.1%. The precision in fall recognition is 76.1%, and the recall is 81.6%. The effect of instant recognition can also be achieved in terms of execution speed. These results prove that the system can effectively monitor children’s dangerous behaviors, and it also has potential application value in fields such as child care and safety monitoring. | en_US |