Constructing finite automata based model for detecting human fall

The fall of a person forms a major cause for serious health decline or injury related death in elderly persons. The existing fall detection algorithms are based on visual sensors. Fall detection using visual sensor has restriction in the coverage region due to privacy. Existing wearable sensor based on accelerometer is not sufficiency for detecting fall as it will not detect fall that occurs due to internal health abnormalities. Both activity and health data differ between persons of different age group. To handle such variation in the data, this paper proposes a novel fall detection scheme based on fuzzy finite automata with probability density function by formulating rule patterns combining the capabilities of automata using both accelerometer and physiological sensors. The proposed scheme provides a systematic approach for continuous monitoring of elderly patients living alone with the ability to detect the fall of the patient, overcoming the demerits of visual sensors.