A Vision-based Human Posture Detection Approach for Smart Home Applications

Effective posture identification in smart home applications is a challenging topic for people to tackle in order to decrease the occurrence of improper postures. Vision-based posture identification has been used to construct a system for identifying people's postures. However, the system complexity, low accuracy rate, and slow identification speed of existing vision-based systems make them unsuitable for smart home applications. The goal of this project is to address these issues by creating a vision-based posture recognition system that can recognize human position and be used in smart home applications. The suggested method involves training and testing a You Only Look Once (YOLO) network to identify the postures. This Yolo-based approach is based on YOLOv5, which provides a high accuracy rate and satisfied speed in posture detection. Experimental results show the effectiveness of the developed system for posture recognition on smart home applications.

@article{Shu2023,
  title     = {A Vision-based Human Posture Detection Approach for Smart Home Applications},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Yangxia Shu and Lei Hu},
  doi       = {10.14569/IJACSA.2023.0141023},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141023}
}