4 research outputs found
Development of a human fall detection system based on depth maps
Assistive care related products are increasingly in demand with the recent
developments in health sector associated technologies. There are several studies
concerned in improving and eliminating barriers in providing quality health care
services to all people, especially elderly who live alone and those who cannot move
from their home for various reasons such as disable, overweight. Among them, human
fall detection systems play an important role in our daily life, because fall is the main
obstacle for elderly people to live independently and it is also a major health concern
due to aging population. The three basic approaches used to develop human fall
detection systems include some sort of wearable devices, ambient based devices or
non-invasive vision based devices using live cameras. Most of such systems are either
based on wearable or ambient sensor which is very often rejected by users due to the
high false alarm and difficulties in carrying them during their daily life activities. Thus,
this study proposes a non-invasive human fall detection system based on the height,
velocity, statistical analysis, fall risk factors and position of the subject using depth
information from Microsoft Kinect sensor. Classification of human fall from other
activities of daily life is accomplished using height and velocity of the subject
extracted from the depth information after considering the fall risk level of the user.
Acceleration and activity detection are also employed if velocity and height fail to
classify the activity. Finally position of the subject is identified for fall confirmation
or statistical analysis is conducted to verify the fall event. From the experimental
results, the proposed system was able to achieve an average accuracy of 98.3% with
sensitivity of 100% and specificity of 97.7%. The proposed system accurately
distinguished all the fall events from other activities of daily life