Human Falling Recognition Based on Movement Energy Expenditure Feature

Algorithm 1

The threshold-based three-level human fall recognition algorithm.

	Step 1: The system is initialized and ready to collect information related to human movement. According to the definition of coordinate direction introduced, the triaxial acceleration information of the upper trunk and the angular velocity information of the body around the x-axis and y-axis were collected, and the combined acceleration at time t, the movement energy expenditure at time t, and the tilt angle of the body away from the z-axis were calculated. Define a data storage space and store the combined acceleration, human movement energy expenditure, and body tilt angle in the form of a sliding time window within 2 seconds before the moment. When the new data arrive, the sliding time window moves forward, the new data are stored, and the earliest data storage space is released.
	Step 2: Test whether the first impact occurs at the current moment: if , there is no impact state, and no fall will occur. Continue monitoring and return to Step 1. If , the first impact is confirmed, and a fall may occur, enter Step 3.
	Step 3: Measure the energy expenditure of human movement calculated at the current moment: if the dynamic change of body movement is not large, the energy expenditure of movement is low, and there will be no fall, but only fast walking, running, and so on. Continue monitoring and return to Step 1. If , the dynamic change of body movement is large, and the energy expenditure is high, a fall may occur. Record the time at this time as t0, and enter Step 4.
	Step 4: The movement information of the human body in the first 2 seconds of the time is recorded, and the tilt angle of the body from the z-axis is denoted as below.
	Step 5: Test whether the impact state is over and whether it is multiple impact falls. The detection method of whether there is still impact in this step is as follows: obtain the relevant motion information within 1 second after the current moment to detect whether there is impact, that is , and whether it is still valid. If it is true, it means that the fall is a multi-impact fall, and the impact is not over yet, so the detection of the impact state should be continued in Step 5.
	Step 6: If , it is not true; it means that the impact has ended. The end time of the last impact is recorded as . At this point, the relevant information of human motion is continued to be obtained to get the tilt angle of the body from the z-axis in seconds , denoted as .
	Step 7: To determine whether the change condition of body tilt angle meets the characteristics of falling behavior, if and , it is judged that the fall behavior process has occurred. Otherwise, it is judged that the falling behavior did not occur, and real-time monitoring should be continued.