A New Method for Real-Time Fall Detection Based on MediaPipe Pose Estimation and LSTM

Falls are a significant health problem among older adults, leading to serious injuries and adversely affecting both quality of life and public health burdens. Although various fall detection systems have been developed using technologies such as wearable sensors and image processing (computer vision), limitations remain in dimensions of convenience, accuracy, and real-time responsiveness. To overcome these limitations, this research aimed to present a real-time fall detection system that integrates MediaPipe pose estimation technology with a Long Short-Term Memory (LSTM) neural network. The proposed method functioned through two main components. MediaPipe pose estimation technology was applied to detect and track keypoints on the human body from real-time video input; meanwhile, a trained LSTM model was utilized to analyze the sequence of movements of the detected keypoints for classifying and differentiating between fall behaviors and normal activities. The system was trained, and its performance was evaluated using the standard UR Fall Detection Dataset. From experimental results, the proposed system achieved high efficiency in fall detection, with an accuracy of 95.2% on the test dataset. The integrated system had its capability to detect all actual fall events (with a recall of 100%). Its false positive rate was low. Compared to other research, the proposed method provided higher accuracy. These results indicated that the proposed system has the potential for practical application as an effective tool for real-time fall alerts, enabling timely assistance for those injured from falls.

@article{Sirikongtham2025,
  title     = {A New Method for Real-Time Fall Detection Based on MediaPipe Pose Estimation and LSTM},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Puwadol Sirikongtham and Apichaya Nimkoompai},
  doi       = {10.14569/IJACSA.2025.0160811},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160811}
}