Our culture is experiencing a fast growing problem with falls, which has generated a lot of interest in the medical field. As we age or if we have pre-existing medical conditions, such as reduced muscle strength, the possibility of falling becomes more of a risk. The need for fall detection systems is growing as the world's population ages. A sudden slip or loss of stability while moving could be the cause of a fall. Several researches have been presented as a measure to alert people of a fall as a speedy post-fall remedy. As a result, numerous fall detection systems have been developed. We provide a Visual input-based fall detection system to address this issue. We have utilized the LR-CNN-PCA Hybrid approach in order to reach the maximum levels of accuracy and effectiveness. The system's robustness and generality were constrained by using wearable technology in the majority of earlier tests. Our method stands for a development in smart technology for older individuals who are single. The ability of our machine learning-based recognition system to identify falling behavior in the video further supports its viability and effectiveness.

Fall detection, Linear Regression, Principal Component Analysis.