A Novel Approach for Human Fall Detection and Fall Risk Assessment

There are several studies concerned in providing quality health care services to all people. Among them, human fall detection systems play an important role, 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 fall detection systems include some sort of wearable, ambient or non-invasive based devices. Most of such systems are 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 fall detection system based on the height, velocity, statistical analysis, fall risk factors and position of the subject from depth information. The proposed algorithm also utilizes fall risk factors of the user, during fall detection process to classify the subject with chances of fall such as high fall risk or low fall risk level. Thus, making the system adaptable to the physical condition of the user. The proposed system also performs a fall injury assessment after the fall event to alert for appropriate assistance and includes a fall risk assessment tool which can work independently to predict falls or to classify people with their fall risk levels. 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%.