Improved user adaptable human fall detection and verification using statistical analysis

Abstract

Human fall detection systems are an important part of human monitoring systems especially for elderlies. Different studies were conducted using varieties of sensor to develop systems to accurately classify unintentional human falls from other activities of daily life. The major issues with the current studies using depth maps were the use of single threshold based algorithms and highly complex machine learning to detect falls. Therefore, the available systems cannot cater for the detection of fall events from people with different physical capabilities and differing environments they are living. This study proposes a user adaptable fall detection system with a statistical analysis based fall verification to overcome the issues of related works.