A surveillance algorithm for fall detection and initiation of an e-mail

Demographic patterns are demonstrating that the world is a maturing society. Advances in medical treatments extend people's life spans. Elderly care is a burden on families' and states' budget. Moreover, elder people want to age in their place without the breach of their privacy and losing independency. Statistically unexpected falls happen to one third of individuals in excess of 65. This thesis focuses on fall problem on elderly, who age at their place. Smart assisting for elderly is an essential need for health care and emergency response when needed. Since sleep assistance is a complex subject, this project covers only the part that, if they fall while standing or sitting they would get help. Thus this thesis aims to help people age in their place by providing fall detection via image processing. The approach can be described as data analysis and programming an algorithm in the area of fall detection. The system analyzes minimum bounding rectangle of a moving object, considers aspect ratio, centroid and diagonal angle. In the literature fall algorithms use computationally expensive algorithms to distinguish the focused person in the image. In this study distinguishing an inactive person is not included considering that a fall necessarily contains motion. The person of interest is distinguished with image differencing. However this technique amplifies the noise in the binarized image. This issue is eliminated using Gaussian filtering on differenced image. Due to experiment constraints an adequate amount of statistics was not possible to collect. However human movements can be imitated with computer software literally. For academic purposes open access to this software can make statistics available for fall algorithms. A series of scenarios of fall is presented in section 2.7.4 and for each category a sample was recorded. Scenarios are a total of 20 with 4 recoveries and 5 fall-like cases. Among them only 11 are real fall cases and they are all detected as true positives besides one. The rest 9 cases are either with recovery or fall like cases and only one of them gives a false positive alarm. Hence the true positive percentage is 91 per cent while the false positive ratio is 11, 1 per cent.