Developing an iPhone smartphone based fall detection algorithm

Many researchers work on human motion detection and recognition systems and have been trying to use these systems in order to solve problems in health services. Falls as one of the major health problems are especially seen more frequently among elderly people and reduce the quality of life dramatically with causing serious damages. Therefore, immediate medical intervention is required to victims of falls only in this way it is possible to minimize emotional and mental traumas may arise from physical causes. Immediate intervention is only provided when the fall is noticed and authorities are informed instantly. In this study, we developed an algorithm that automatically detects falls by benefiting from the gyroscope and accelerometer sensor data in smart phones. Tests which involve daily living activities and falls data are applied to the algorithm, and it is seen that falls are distinguished from daily living activities with 100% accuracy.