Human Fall Detection System over IMU Sensors Using Triaxial Accelerometer

A sudden increase in the number of deaths over the past few years by slipping and falling, especially in case of patients in hospitals and aged people at homes, is a serious concern and calls for the need of an autonomous system for detection of fall and alerting caretaker in case of emergency. We propose an algorithm which, first, derives features from an input stream of data sensed and uses it in learning of our system and further, provides it with the capability of classifying a sequence into either fall or activity of daily living sequence implemented using support vector machine. We propose a space and time efficient system, minimizing its cost by using only 3-axial accelerometer as sensor. Choice of type and number of features along with their operational complexity is a crucial factor for our system. Performance analysis is done by first training our system and then testing its accuracy in classifying test sequences using machine learning algorithm.

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