With the continuous development of the aging society, it is expected that the proportion of empty-nest elderly households will reach 90% by 2030, by which time our elderly households will be empty-nest. According to statistics, more than 1/3 of the elderly people over the age of 65 experience falls every year, and 2/3 of the elderly are accidentally killed by falls, and this proportion is even more common among the elderly over 75 years old. Up to 70%.
Fall detection is an implementation of a home terminal in a remote health monitoring system. It involves multiple fields, including research on signal acquisition and processing, signal feature extraction, and data transmission. There are many fall detection technologies, which can be classified from the channel of signal acquisition, and fall detection technologies can be divided into three categories: fall detection based on video images. The disadvantage of this method is that it cannot guarantee user privacy and security and the quality of video images Light and other environmental impacts are large; the fall detection based on acoustic signals is complicated to install and relatively large in the early stage; the fall detection based on wearable devices is more prominent in the applicable environment and the degree of interference to users than the previous two methods advantage. Comprehensive comparison of various detection methods, remote monitoring of the health of the elderly based on wearable detection methods is more suitable.
The fall detection module is mainly composed of an acceleration acquisition unit, a microprocessor unit, a wireless communication unit, and a remote fall monitoring background, etc. The overall module is implemented with a lithium battery. The system collects acceleration by the acceleration acquisition unit, preprocesses the signal through the microprocessor unit, and the suspicious data extracted by the preprocessing is transmitted to the remote fall monitoring background through the wireless communication unit for final analysis and processing. When a fall is detected, the system Can automatically trigger the alarm item.
During the fall of the human body, the three vectors of acceleration, velocity and displacement of the object in all directions will change. In fact, if it is only based on the change of acceleration in all directions, it is difficult to fully distinguish the falling motion. By accelerating the acceleration in the time domain once to find the speed (v), and twice integrating to find the displacement (s), in order to improve the accuracy of the system.
The acceleration collected by the acceleration sensor includes the two parts of the acceleration of the earth's gravity and the acceleration caused by the movement of the human body, and these two parts exist at any time. Based on the acceleration of human body motion, a three-dimensional human action model [6] is established. According to the three orthogonal measurement directions of the acceleration sensor, a three-dimensional coordinate system can be established.
Generally, when the device is properly worn, when the subject is in a stationary or horizontally uniform motion state, the acceleration in the Y direction is represented by the acceleration of gravity (g), and the acceleration in the horizontal direction is 0. When the subject falls, if only the change of the acceleration value in the beginning and end states is considered, the longitudinal change range is from 1g to 0g, and the change of the horizontal component (x or z) is 0g to 1g.
A fall detection module based on a three-dimensional acceleration sensor, a microprocessor and a wireless communication module is constructed, which can better distinguish between daily activities and fall events.
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