Fall Detection Based on Movement and Smart Phone Technology

Nowadays, recognizing human activities is an important subject; it is exploited widely and applied to many fields in real-life, especially health care or context aware application. Research achievements are mainly focused on activities of daily living which are useful for suggesting advises to health care applications. Falling event is one of the biggest risks to the health and well being of the elderly especially in independent living because falling accidents may be caused from heart attack. Recognizing this activity still remains in difficult research area. Many systems which equip wearable sensors have been proposed but they are not useful if users forget to wear the clothes or lack ability to adapt themselves to mobile systems without specific wearable sensors. In this paper, we develop novel method based on analyzing the change of acceleration, orientation when the fall occurs. In this study, we recruit five volunteers in our experiment including various fall categories. The results are effective for recognizing fall activity. Our system is implemented on Google Android smart phone which already plugged accelerometer and orientation sensors. The popular phone is used to get data from accelerometer and results show the feasibility of our method and contribute significantly to fall detection in Health care.