Application of Artificial Intelligence in Human Fall Detection

Falls are a significant public health concern, particularly among older adults, who are more vulnerable to injury and death resulting from falls. According to the World Health Organization (WHO), falls are the second leading cause of accidental or unintentional injury deaths worldwide. With the global population aging rapidly, there is an urgent need to develop reliable and accurate fall detection systems.

The current approaches for fall detection include wearable and non-wearable devices, such as sensors, cameras, and accelerometers. However, these methods often suffer from limitations, such as low accuracy, false alarms, and inability to detect certain types of falls, such as falls from a seated position or falls on soft surfaces. The use of artificial intelligence (AI) algorithms has shown great potential in addressing these limitations by providing more accurate and reliable fall detection.

The application of AI in fall detection is a growing research area, with many recent studies exploring the use of machine learning, deep learning, and computer vision algorithms in human fall detection. These studies have shown that AI algorithms can significantly improve the accuracy and reliability of fall detection systems, particularly when combined with wearable and non-wearable devices.

Faculty Supervisor:

Alireza Ghasemi

Student:

Partner:

ISEN

Discipline:

Engineering

Sector:

Artificial Intelligence; Health and Related Sciences & Technology; Information and Communications Technology

University:

Dalhousie University

Program: