Research Of Human Fall Detection And Alarm Application On IOS Platform

Along with New Century arrived, family planning policy has been implemented effectively. However, population aging arrives in advance and leads to many problems of the elderly. Specially, the health of the elderly is a major concern for society. Accidental falls become an important factor affecting the health of the elderly. Serious consequences caused by falls not only make the elderly suffer great physical and psychological trauma, but also bring families suffering and economic burden. Designing fall detection software has a better effect on the prevention of sudden illness for the elderly caused by a fall. Therefore, fall detection technologies and detection products gradually become the focus of the study of human health intelligent monitoring for researchers.In this thesis, we investigate the existing fall detection technology and related products and evaluate the advantages and disadvantages of these technologies. Currently, the fall detection technologies can be classified into three categories:video surveillance, audio-vibration-based identification and wearable detection device. Wearable detection device is deemed to be the main research trend currently. The rise of smart-phones, especially the iPhone built-in accelerometer and gyroscope based on the iOS platform has opened up new ideas for fall detection research. Comparing with traditional wearable detection device, detection on cell phone is more simple to use and easy to carry.In this thesis, we study and discuss the feasible and reasonable of the fall detection and alarm application based on the iOS platform. We analyze the variation between human motion and acceleration, and distinguish Activities of Daily Life, the phone’s operating actions and falls. We achieve the median filter designed by Objective-C language on iOS platform. This thesis gives the complete research flow of fall detection and alarm application on iOS platform.