In the modern information era, fall accidents are one of the leading causes of injury, disability and death to elderly individuals. This research focuses on object detection and recognition using deep neural networks, which is applied to the theme of fall detection. We propose a deep learning algorithm with the capability to detect fall accidents based on the state-of-the-art object detector, YOLOv3. Our system is tested on a challenging video database with diverse fall accidents under different scenarios and achieves an overall accuracy rate of 63.33%. The proposed deep network shows great potential to be deployed in real-world scenarios for health monitoring.
|Title of host publication
|Deep learning-based fall detection
|Place of Publication
|In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12.
|World Scientific, Singapore
|Number of pages
|Published - 15 Aug 2020