Human fall detection using multimodal datasets

• Author(s) / Creator(s)

  • Pacheco Jiminez, Ana Janet

• Falling events are the leading cause of fatal and non-fatal injuries among elderly, and without timely medical care people unable to recover by themselves, it may mean suffering from serious consequences. Designing reliable fall detection systems may help reduce the time for a person to receive an opportune medical care. Falls are events which might be described using multiple sensors, this is because the problem itself involves many features, such as the sudden change in human’s body position which can be sensed by tracking acceleration changes in the three orthogonal directions, or while monitoring activities of daily living of elderly people. This project is aimed to compare the performance of a fall detection system based on a multimodal dataset using common deep learning algorithms, such as Convolutional Neural Networks and Multi-Layer models and feeding them with image data only (RGB and Depth Data), versus when adding signal data from an accelerometer sensor.

• Date created

  2022

• Subjects / Keywords

  • multi-modal fall detection system
  • Convolutional Neural Network
  • multi-layer perception
  • RGB
  • depth data
  • accelerometer sensor

• Type of Item

  Research Material

• DOI

  https://doi.org/10.7939/r3-n169-4337

• License

  Attribution-NonCommercial 4.0 International