Research On Remote Fall Detection Technology

As the society continues to age, the percentage of elder people also correspondingly increases every year. This brings about the issue of mobility among the elder people in their daily activities. As a person grows old, it will bring about a mental and behavioral change, where they become prone to falling down. The effect of a fall is one important factor that will have an impact on the mental health of an elder person. When an elder person falls down, it is important to seek help in the quickest time possible in order to minimize injuries and damage. In recent years in the field of biomedical engineering, the studying and researching of heart rate fluctuations of a person's condition during and after a fall is becoming a popular research subject. This has created a new sense of direction and objective in the research of fall detection technologies. The purpose of this research paper is to use fall detection technology to provide some form of protection in the daily activities of an elder person, and ensuring safety. Thus, this paper after conducting a comprehensive analysis by taking into account internal and external factors has derived the following main areas of research:1. In analyzing various fall detection and other concerning technologies, the paper has compared video image capturing, acoustic analysis and wearable fall detection technologies and have concluded that wearable fall detection technology is more practical in detecting falls. Furthermore, fall detection technique analysis can be split into two parts. One part is to use a forecasting mechanism where it uses a terminal to collect and analysis data. The other is the use of supervision or monitoring mechanism for the final analysis and processing.2. During the research fall detection technology, the paper introduces the data preprocessing mechanism. After researching and analysis the SVM algorithm, the paper proposes to use a One-Class SVM classification algorithm to analyse the original or raw data collected by the terminal.3. The analysis of the human motion energy expenditure will be ultimately used to determine a fall, but also will take into account other analysis information such as the body posture, condition and other information. The reason being using human motion energy as the primary determinant is because studies have found the energy used during human motion and the speed at which a human move has a very high linear correlation.4. Through the research in fall detection techniques, the paper will be discussing in Chapter 4, the use of a modular design approach to design the health supervisory system for fall detection.Through researching and experimenting, the paper has achieved its intended initial objectives in the developing the algorithm for fall detection, but the system design needs to be further improved, in order to becoming the technology in developing build health monitoring systems for the elderly to ensure their safety. Also, this technology has the potential to be used widely in other applications as well.