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Interrupt-Driven Fall Detection System Realized via a Kalman Filter and kNN Algorithm

According to the requirement of low power and accuracy for fall detection, an activity model based on three-dimensional attitude angles is introduced, and the difference in attitude angles and signal vector magnitude of acceleration between daily activities and falls are compared. Second, a sensor board integrated with MPU6050 and ZigBee which can collect and transmit the tri-axial accelerations and angular velocities of human activities to the server at low-power is developed. Finally, a fall detection system running on the server is developed via a Kalman filter and kNN algorithm. It is proved by experiment that the accuracy of the system is 98.2%, while its sensitivity and specificity are 96.2%, and 99.2%, respectively.