elderly fall detection

A Deep learning-based elderly fall detection using optimized Machine learning model


  •   A framework is being developed specifically for detecting falls or non-falls in elderly individuals.
  •   The use of multimodal data will help to combine different types of information, leading to better accuracy in detecting objects.
  •   The proposal will suggest a machine learning technique that can efficiently learn and extract features from multimodal data.
  •   Before performing object detection, a preprocessing stage will be carried out. This stage involves the identification and restoration of noisy data.
  •   The performance of the proposed framework will be evaluated through extensive experiments on two commonly used datasets.

  • Description

The elderly population worldwide is greatly affected by falls, which can result in functional impairment, reduced mobility, independence, and quality of life if not assisted. It would be advantageous to have a computerized system that can quickly and accurately identify and categorize falls. This would be particularly useful for keeping an eye on older individuals and accelerating the process of getting them help, which would lower the chances of them experiencing serious damage.